Overcoming Poverty in Multidimensional Poverty Interventions through Self-assessment and Mentoring

Una organización de microfinanzas en Paraguay ha desarrollado el "Semáforo de Eliminación de Pobreza" (PS), una herramienta tecnológica innovadora que permite a las familias autoevaluar su nivel de pobreza multidimensional y comenzar un proceso de tutoría integrado que tiene el objetivo de empoderar a las familias para eliminar la pobreza multidimensional en función de lo que los participantes valoran más. Durante el programa, un mentor trabaja con los participantes para diseñar un plan familiar personalizado para identificar sus desafíos más importantes para superar sus privaciones. El PS coloca el desarrollo humano y la eliminación de la pobreza como el objetivo principal de la intervención. Este artículo tiene tres objetivos. Primero, presenta la herramienta como una métrica multidimensional y una metodología para la intervención de la pobreza con el propósito explícito de fomentar la reflexión y promover la agencia. En segundo lugar, usa el Enfoque de Capacidad para explorar el potencial de la intervención del PS para aumentar la agencia y disminuir la privación. En tercer lugar, presenta los resultados de un proyecto de investigación en curso que evalúa la efectividad del programa para ayudar a los participantes a superar la pobreza. Esta parte empírica se basa en datos recopilados entre agosto de 2015 y junio de 2017 de más de 9,000 clientes de micro finanzas y analizados mediante regresiones OLS. Los resultados indican que la participación en el programa está asociada con una mayor probabilidad de superar la pobreza multidimensional.A microfinance organization in Paraguay has developed the “Poverty Stoplight” (PS), an innovative technological tool that allows families to self-assess their level of multidimensional poverty and start an integrated mentoring process whose goal is to empower families to eliminate multidimensional poverty based on what the participants value the most. During the program, a mentor works with participants to design a customized family plan to identify their most significant challenges in order to overcome their deprivations. The PS places human development and poverty elimination as the main objectives for the intervention. This paper has three elements. First, it presents the methodology for poverty intervention and the detailed tool (i.e. a multidimensional metric) used to encourage reflecting and promoting agency. Second, it uses the Capability Approach to explore the potential of the PS intervention to increase agency and decrease multidimensional deprivations. Third, it presents results from an ongoing research project that evaluates the program’s effectiveness in helping participants overcome poverty. This empirical part is based on data collected between August 2015 and June 2017 from over 9,000 microfinance clients and analyzed using the technique of OLS regressions. The results indicate that participation in the program is indeed associated with a higher probability of overcoming multidimensional poverty

Multi-Echelon Inventory Optimization Using Deep Reinforcement Learning

In this chapter, we provide an overview of inventory management within the pharmaceutical industry and how to model and optimize it. Inventory management is a highly relevant topic, as it causes high costs such as holding, shortage, and reordering costs. Especially the event of a stock-out can cause damage that goes beyond monetary damage in the form of lost sales. To minimize those costs is the task of an optimized reorder policy. A reorder policy is optimal when it minimizes the accumulated cost in every situation. However, finding an optimal policy is not trivial. First, the problem is highly stochastic as we need to consider variable demands and lead times. Second, the supply chain consists of several warehouses incl. the factory, global distribution warehouses, and local affiliate warehouses, whereby the reorder policy of each warehouse has an impact on the optimal reorder policy of related warehouses. In this context, we discuss the concept of multi-echelon inventory optimization and a methodology that is capable of capturing both, the stochastic behavior of the environment and how it is impacted by the reorder policy: Markov decision processes (MDPs). On this basis, we introduce the concept, its related benefits and weaknesses of a methodology named Reinforcement Learning (RL). RL is capable of finding (near-) optimal (reorder) policies for MDPs. Furthermore, some simulation-based results and current research directions are presented

3D polymer structures with variable permittivity at terahertz frequencies

Titanium dioxide (TiO2) powder has been blended with polydimethylsiloxane (PDMS) to manufacture a composite polymer with variable permittivity. Vector network analyser measurements taken between 0.75-1.1 THz quantify the relationship between TiO2 concentration and complex permittivity of the resultant material. Complex 3D structures have been produced with a casting process. Applications for the tunable-permittivity polymer include dielectric regions in photonic and plasmonic devices operating at terahertz frequencies as well as single pixel imaging systems

Profits Over Development?

Seit Beginn der 1990er Jahre gewinnt Privatsektorentwicklung im Rahmen der staatlichen Entwicklungspolitik immer mehr an Bedeutung, damit geht auch das Florieren der dazugehörigen Durchführungsorganisationen Development Finance Institutions (DFI) einher. Der Spagat zwischen Gewinnstreben und entwicklungspolitischen Effekten liegt in der Natur der DFI und ist gleichzeitig ihre größte Herausforderung. In der vorliegenden Arbeit wird untersucht, inwiefern die beiden schon lange etablierten Entwicklungsfinanzierungsinstitutionen CDC Group plc. (Großbritannien) und DEG (Deutschland) ihrer Rolle und ihrem Auftrag gerecht werden. Unter Zuhilfenahme von Konzepten aus der Policy Analyse gehen die Autorinnen den Fragen nach, nach welchen Kriterien die DFI ihre Investitionsprojekte auswählen, was für sie entwicklungspolitische Relevanz bedeutet und wie sie mit Transparenz und Rechenschaftspflichten gegenüber Politik und Öffentlichkeit umgehen. Im Zuge der deskriptiven Analyse wird klar, dass die beiden Entwicklungsfinanzierungsinstitutionen große Unterschiede aufweisen; die identifizierten Unzulänglichkeiten werden schlussendlich als Ausgangspunkt für Politikempfehlungen genommen.At the beginning of the 1990s a new trend in the field of governmental development policy was born: Private Sector Development. At the same time, implementing agencies called Development Finance Institutions (DFI) started to boom. The balancing act between achieving high rates of return and creating development impact is in their nature and constitutes their major challenge. The authors of this paper discuss whether the already long time ago established CDC Group plc. (UK) and DEG (Germany) are able to come up to their roles and fulfil their missions. The study is done by taking recourse to the policy analysis and asking several questions: which criteria do the DFI use in order to choose the projects they invest in, what do they consider as development impact and how do they deal with transparency and accountability in relation to government and public. The descriptive analysis shows that there are substantial differences between the two DFI; the identified shortcomings are taken as a starting point for policy recommendations

Content Management System Tools to Enhance Student Learning in Higher Education

This investigation will examine the importance of the integration of tools that are vital to student retention, academic success, and the efficient movement through the graduation pipeline. This work will evaluate some of the current CMS tools used in higher education and how those tools are assisting students, faculty, and staff navigating through the system. Empirical evidence suggests that leveraging CMS tools benefits students, employers, and funders of higher education because the quality of data collected is relevant and useful (Alalwan, Thomas, & Weistroffer, 2014). This research is divided into seven distinct sections: Defining Content Management Systems, Evolution of CMS tools in the Higher Education Process, Current Generation of CMS tools in the Higher Education Process, Role and Amalgamation of CMS Tools in the Graduation Pipeline, The Digital Campus in Support of Graduation Rate Improvement, Constructing the Effective Digital Campus, and Barriers Stifling Full CMS Tool Integration. Suggestions for the future of this area are discussed in the summative conclusion

Responding to the Forced Pivot of Higher Education Planners and Practitioners Serving Students in the Aftermath of COVID-19

This research provides valuable insights into the impact of COVID-19 on the education system, the need for adaptation, and the importance of various aspects such as experiential learning, emotional well being, mental health, workplace and online education. It offers recommendations and considerations for policymakers and practitioners in higher education to navigate the post-pandemic world successfully

Final report

“Poverty Stoplight” is both a metric and a methodology. Program staff works directly with families in poor communities to complete an easy-to-use, picture-based survey to evaluate poverty levels across a variety of dimensions and indicators. Indicators are self-assessed by the individuals and families who participate, creating data from the bottom up; the method includes measures of agency and organization, is designed to include only indicators that are actionable and; the survey is designed to empower individuals completing the self-assessment, particularly female heads of households. The report details methodology, activities, outputs and outcomes of the project

Fully dynamic reorder policies with deep reinforcement learning for multi-echelon inventory management

The operation of inventory systems plays an important role in the success of manufacturing companies, making it a highly relevant domain for optimization. In particular, the domain lends itself to being approached via Deep Reinforcement Learning (DRL) models due to it requiring sequential reorder decisions based on uncertainty to minimize cost. In this paper, we evaluate state-of-the-art optimization approaches to determine whether Deep Reinforcement Learning can be applied to the multi-echelon inventory optimization (MEIO) framework in a practically feasible manner to generate fully dynamic reorder policies. We investigate how it performs in comparison to an optimized static reorder policy, how robust it is when it comes to structural changes in the environment, and whether the use of DRL is safe in terms of risk in real-world applications. Our results show promising performance for DRL with potential for improvement in terms of minimizing risky behavior

Synthesis and Characterization of Dye-Labeled Nucleotides to Follow Protein Activity

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Overcoming Poverty in Multidimensional Poverty Interventions through Self-assessment and Mentoring

A microfinance organization in Paraguay has developed the “Poverty Stoplight” (PS), an innovative technological tool that allows families to self-assess their level of multidimensional poverty and start an integrated mentoring process whose goal is to empower families to eliminate multidimensional poverty based on what the participants value the most. During the program, a mentor works with participants to design a customized family plan to identify their most significant challenges in order to overcome their deprivations. The PS places human development and poverty elimination as the main objectives for the intervention. This paper has three elements. First, it presents the methodology for poverty intervention and the detailed tool (i.e. a multidimensional metric) used to encourage reflecting and promoting agency. Second, it uses the Capability Approach to explore the potential of the PS intervention to increase agency and decrease multidimensional deprivations. Third, it presents results from an ongoing research project that evaluates the program’s effectiveness in helping participants overcome poverty. This empirical part is based on data collected between August 2015 and June 2017 from over 9,000 microfinance clients and analyzed using the technique of OLS regressions. The results indicate that participation in the program is indeed associated with a higher probability of overcoming multidimensional poverty