Rolando Manuel Gonzales Martinez

• Gonzales Martinez, Rolando; D’espallier, Bert & Mersland, Roy (2024). Informal Insurance and Loans in Savings Groups: A Longitudinal and Quasi-experimental Approach Identifies the Role of the Welfare Fund for the Extreme Poor. Review of Development Finance. ISSN 1879-9337. 14(1), s. 1–10.
• Gonzales Martinez, Rolando (2024). Apocalypse unleashed: a critical perspective on complexity science, catastrophes, and black swan events in international business. Critical Perspectives on International Business. ISSN 1742-2043. 20(1), s. 94–120. doi: 10.1108/cpoib-03-2022-0014.
• Anand, Paul; Allen, Heidi L; Ferrer, Robert L; Gold, Natalie; Gonzales Martinez, Rolando & Kontopantelis, Evangelos [Vis alle 8 forfattere av denne artikkelen] (2022). Work-related and personal predictors of COVID-19 transmission: Evidence from the UK and USA. Journal of Epidemiology and Community Health. ISSN 0143-005X. 76(2), s. 152–157. doi: 10.1136/jech-2020-215208. Fulltekst i vitenarkiv Vis sammendrag

  Objective To develop evidence of work-related and personal predictors of COVID-19 transmission. Setting and respondents Data are drawn from a population survey of individuals in the USA and UK conducted in June 2020. Background methods Regression models are estimated for 1467 individuals in which reported evidence of infection depends on work-related factors as well as a variety of personal controls. Results The following themes emerge from the analysis. First, a range of work-related factors are significant sources of variation in COVID-19 infection as indicated by self-reports of medical diagnosis or symptoms. This includes evidence about workplace types, consultation about safety and union membership. The partial effect of transport-related employment in regression models makes the chance of infection over three times more likely while in univariate analyses, transport-related work increases the risk of infection by over 40 times in the USA. Second, there is evidence that some homerelated factors are significant predictors of infection, most notably the sharing of accommodation or a kitchen. Third, there is some evidence that behavioural factors and personal traits (including risk preference, extraversion and height) are also important. Conclusions The paper concludes that predictors of transmission relate to work, transport, home and personal factors. Transport-related work settings are by far the greatest source of risk and so should be a focus of prevention policies. In addition, surveys of the sort developed in this paper are an important source of information on transmission pathways within the community.

• Gonzales Martinez, Rolando (2022). How good is good? Bayesian machine-learning estimation of probabilistic benchmarks in noisy datasets and an application to nanofinance+. Systems and Soft Computing (SASC). ISSN 2772-9419. 4. doi: 10.1016/j.sasc.2022.200036. Vis sammendrag

  Benchmarks are reference standards that are calculated using key performance indicators (KPIs). Calculating benchmarks in noisy datasets is challenging because noise in KPIs reduces the accuracy and precision of benchmarks. The purpose of this research is to propose a two-step methodology for calculating probabilistic benchmarks in noisy datasets. The research methods are based on swarm optimization and Bayesian machine-learning. Swarm optimization simulates the behavior of birds and applies a novel double-hyperbolic undersampling algorithm to denoise KPIs. Bayesian machine-learning estimates probabilistic benchmarks with denoised KPIs through relevance vector machines. The practical implementation of the methods is illustrated with an application to a database of nanofinance+. The results indicate that the proposed methodology is able to denoise KPIs, estimate probabilistic benchmarks, and properly identify the continuous and discrete factors influencing the accuracy and precision of benchmarks.

• Gonzales Martinez, Rolando; D'Espallier, Bert & Mersland, Roy (2022). What Drives Profits in Savings Groups? Bayesian Data Mining Evidence from the SAVIX Database. Review of Development Finance. ISSN 1879-9337. 11(2), s. 39–57. Vis sammendrag

  Savings groups provide savings and loans to low-income individuals in rural and peri-urban areas. This study applies Bayesian data mining methods to a database of more than 200,000 savings groups with the aim of identifying which micro, meso, and macro factors are associated to profit generation in the groups. The results show that the facilitation mechanisms of development agencies and the macro-economic environment are more important than internal group dynamics for profit generation. The findings suggest that the focus of development agencies on bottom-of-the-pyramid groups creates wealth for communities in countries with a more dispersed and rural population. However, the generation of profits depends on the graduation of groups and the facilitation model implemented by a development agency.

• Ravelli, Edsard & Gonzales Martinez, Rolando (2021). Environmental risk factors of airborne viral transmission: Humidity, Influenza and SARS-CoV-2 in the Netherlands. Spatial and Spatio-temporal Epidemiology. ISSN 1877-5845. doi: 10.1016/j.sste.2021.100432. Fulltekst i vitenarkiv
• Gonzales Martinez, Rolando; D'Espallier, Bert & Mersland, Roy (2020). Bifurcations in business profitability: An agent-based simulation of homophily in self-financing groups. Journal of Business Research. ISSN 0148-2963. doi: 10.1016/j.jbusres.2020.06.051. Fulltekst i vitenarkiv
• Samonte, Francis Gregory R.; De Guzman, Ma. Teresa & Gonzales Martinez, Rolando (2020). A population level analysis of mental health and non-communicable disease (NCD) in the Philippines using predictive modelling analysis . International Journal of Psychosocial Rehabilitation. ISSN 1475-7192. 24(6), s. 12149–12158. doi: 10.37200/IJPR/V24I6/PR261196.
• Anand, Paul; Saxena, Swati; Gonzales Martinez, Rolando & Dang, Hai-Anh H. (2020). Can Women’s Self-help Groups Contribute to Sustainable Development? Evidence of Capability Changes from Northern India. Journal of Human Development and Capabilities. ISSN 1945-2829. 21(2), s. 137–160. doi: 10.1080/19452829.2020.1742100. Fulltekst i vitenarkiv
• Gonzales Martinez, Rolando (2019). Which social program supports sustainable grass-root finance? Machine-learning evidence. International Journal of Sustainable Development and World Ecology. ISSN 1350-4509. s. 1–7. doi: 10.1080/13504509.2019.1706059. Fulltekst i vitenarkiv Vis sammendrag

  Resources for development are used efficiently when social programs help to promote at the same time the sustainability of grass-root financial associations at the bottom of the pyramid. This study applies machine-learning to a worldwide database of grass-root associations in order to identify which social programs are good predictors of financial returns in the groups. The results indicate that education, income-generating activities and health programs are the most frequent programs provided by development agencies. Business training is not the most frequent intervention applied to grass-root associations, but it is in fact the most important social program to encourage financial sustainability, particularly after a development agency stops working with a group and leaves the community. Theoretical and practical implications of the findings are discussed.

• Gonzales Martinez, Rolando; Aguilera-Lizarazu, Gabriela; Rojas-Hosse, Andrea & Aranda Blanco, Patricia (2019). The interaction effect of gender and ethnicity in loan approval: A Bayesian estimation with data from a laboratory field experiment. Review of Development Economics. ISSN 1363-6669. doi: 10.1111/rode.12607. Vis sammendrag

  Microfinance targets women and uses loan provision as a tool for empowerment, which translates into better household nutrition, improved education, and a scale down of domestic violence. However, ethnic discrimination in microfinance may exist in countries with a segregated indigenous population. We assessed this possibility with a field experiment in Bolivia. The controlled laboratory experiment evaluated whether credit officers rejected microloan applications based on the interaction effect of ethnicity and gender of potential borrowers. Point estimates of a Bayesian mixed‐effects logistic regression, estimated with the experimental data, indicate that nonindigenous women have double the chance of loan approval, but indigenous women have only 1.5 times the chance of loan approval when compared with men. While the findings about gender are limited, the evidence for the interaction of gender and ethnicity is more robust and suggests the existence of positive taste‐based discrimination favorable for nonethnic women in Bolivia. We conclude that the affirmative actions towards women promoted by development agencies and microfinance institutions must not overlook ethnicity as an important factor for financial policies of sustainable development. In practice, these policies should be aimed at identifying and reducing both social desirability bias and the structural barriers to financial inclusion that indigenous women may face when trying to obtain access to a loan.

• Gonzales Martinez, Rolando & Rojas-Hosse, Andrea (2019). Inflation shocks and income inequality: An analysis with genetic algorithms and Bayesian quantile regressions. African Journal of Economic and Management Studies. ISSN 2040-0705. 10(2), s. 226–240. doi: 10.1108/AJEMS-10-2018-0299. Fulltekst i vitenarkiv
• Gonzales Martinez, Rolando (2018). The Wage Curve, Once More with Feeling: Bayesian Model Averaging of Heckit Models. Econometric Research in Finance. ISSN 2451-1935. 3(2), s. 79–92. doi: 10.33119/ERFIN.2018.3.2.1. Fulltekst i vitenarkiv

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• Gonzales Martinez, Rolando; D'Espallier, Bert & Mersland, Roy (2019). The macro/meso/micro determinants of the performance of savings groups.
• Gonzales Martinez, Rolando (2020). Data-driven & Theory-driven Science: Artificial Realities and Applications to Savings Groups. University of Agder. ISSN 978-82-8427-014-2. Fulltekst i vitenarkiv Vis sammendrag

  The scientific process is neither unique nor nomic. Two processes of scientific inquiry are theory-driven and data-driven science. This dissertation analyzes savings groups using theory-driven and data-driven methods. Simulated realities-based on data-driven theory-are used to understand the emerging dynamics of savings groups. Savings groups are grassroots, community-based organizations composed of 15 to 30 members. These organizations-usually supported by international development agencies-have weekly meetings during a cycle of operations that typically lasts a year. In the groups, savings are kept in two funds: a fund for loans and a social welfare fund that covers life-cycle events. The findings of Papers A to D in this dissertation provide new large-sample evidence about savings groups, their dynamics, and the factors affecting their financial performance. In practice, the results of Paper A to D shed light on the best policies to promote sustainable development with informal finance in a cost-effective way. A theory-driven approach indicates that the social fund in savings groups stimulates loan allocation among risk-sharing members, while implicitly covering idiosyncratic risks (Paper A). A data-driven approach based on Bayesian data-mining reveals that the macroeconomic environment and the facilitation model of development agencies have a strong influence on the profit-generating capacity of savings groups (Paper B). Machine-learning methods further show that business training is not the most frequent program implemented by development agencies, but it is in fact the most powerful intervention to encourage profits, particularly when a development agency stops working with a group and leaves a community (Paper C). Finally, the simulation of a village with artificial agents indicates that the businesses of savings groups can have higher profits due to the consolidation of social capital and the competitive advantage created through a process of homophily (Paper D). Metatheoretically, the theory-driven and data-driven approaches of this dissertation-and the complementarity between these approaches-contribute to the epistemology of data-intensive science. The dissertation concludes that the gelstaltic and quasi-teleological explanations of the data-driven approach help to the formulation of theories through inductive and abductive reasoning.

• Mersland, Roy; D'Espallier, Bert; Gonzales Martinez, Rolando & Nakato, Linda (2019). What are Savings Groups? Universitetet i Agder.

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