Developing countries are notably more vulnerable to corruption in public procurement due to the lack of effective accountability mechanisms and the absence of robust control structures. Despite efforts to enhance transparency and efficiency, irregularities and inconsistencies persist in electronic procurement systems where suppliers have arranged the auction winner for a tender. We propose a systematic process that includes the creation of metrics that allow measuring the behavior of suppliers in auctions to create embedding suppliers, a graph to model the connections that suppliers have when they participate in a tender together, and a Generative Adversarial Attributed Network (GAAN) to detect anomalous suppliers using a public tenders dataset from the The Ecuadorian Public Procurement Service (SERCOP). The observation period is 2020 when corruption scandals occurred due to purchasing medicines and medical supplies in the health sector to face the COVID-19 pandemic. The study results detected anomalous suppliers in the attributed graph; 9.15% of the total suppliers in the health sector tender dataset were detected as anomalous. The anomalous suppliers have been corroborated with news and reports from the control authorities.

Eradicating poverty in all its forms everywhere remains as the number one Sustainable Development Goal of the 2030 Agenda for Sustainable Development. Developing countries face challenges in measuring the progress of poverty rates at the intra-urban level because they use traditional data collection methods such as censuses that are costly in time and resources. Therefore, local and central governments need ways of producing reliable, accurate, and up-to-date indicators to design effective policies about resource allocation for poverty alleviation programs that prioritize the most vulnerable citizens. For this purpose, we propose to exploit patterns observed in developing countries, where mobile phone usage is pervasive even among the poorest, and the dominant mobile subscription modality is prepaid to purchase airtime credit in advance. Our study analyzes a novel digital source with more than 9M mobile airtime top-up transactions to calculate meaningful indicators of customer economic activity. We aggregate it at the neighborhood spatial resolution to build a regression model to predict the neighborhood socioeconomic status (per capita income). Using a Linear Regression with Regularization L2 (Ridge), we can explain the neighborhood socioeconomic status with a prediction rate of up to 74% for urban neighborhoods of Guayaquil and Quito, Ecuador.

Quantifying income inequalities in developing countries faces challenges regarding data publicly available. Census data, collected every five or ten years, is the only source for socioeconomic indicators. Thus, local authorities need ways of producing more frequently updated indicators. Studies conducted for developed countries (Europe and USA) use Call Detail Records (CDRs) for such a purpose. In our study we propose to exploit patterns observed in developing countries, specifically in Latin America, where mobile phone usage is pervasive even among the poorest and the dominant modality for purchasing mobile airtime is the prepaid scheme (top-ups). We analyze more than 1M top-up transactions together with more than 5K online classified ads for housing sales to predict the socioeconomic status measured at an intra-urban level for 89 neighborhoods. Using a Linear Regression with Regularization L1 (Lasso), we can explain the economic status with a prediction rate up to 71% for urban neighborhoods in Guayaquil, Ecuador. Consequently, we show evidence that top-up transactions provide effective signals to characterize urban neighborhoods socioeconomic status.

Natural disasters have proven that governments, even in developed countries, have difficulties to get up-to-date data about not only affected people but also the location and intensity of infrastructure damage when a country is shook by the nature. Therefore, knowing how mobility patterns are changing, in the post disaster time-frame, is crucial in order to settle rescue centers and send help to the most affected areas. In this scenario, we analyze the relations between human mobility patterns and the effects of an earthquake that shook Ecuador on April 16th, 2016. We do so using more than 11 millions of aggregated call detail records provided by Telefonica. We propose a metric named Reach Score to build timeseries as a way to characterize the residents geographic reach according to their mobile activity. Next, we define the metric Reach Score change, RiSC to capture differences in mobility among two given dates. Our results show that these two metrics calculated on data from the day before and the day after the disaster reflect both the overall change in mobility at the province level and the intensity of infrastructure damage at canton level. In fact, we obtain a Pearson correlation coefficient of r = -0.819 between the metric RiSC and the infrastructure damage score taken from official data.