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This chapter analyzes a geographic quasi-experiment embedded in a cluster-randomized experiment in Honduras. In the experiment, average treatment effects of conditional cash transfers on school enrollment and child labor were large – especially in the poorest experimental blocks – and could be generalized to a policy-relevant population given the original sample selection criteria. In contrast, the geographic quasi-experiment yielded point estimates that, for two of three dependent variables, were attenuated. A judicious policy analyst without access to the experimental results might have provided misleading advice based on the magnitude of point estimates. We assessed two main explanations for the difference in point estimates, related to external and internal validity.

We study research designs where a binary treatment changes discontinuously at the border between administrative units such as states, counties, or municipalities, creating a treated and a control area. This type of geographically discontinuous treatment assignment can be analyzed in a standard regression discontinuity (RD) framework if the exact geographic location of each unit in the dataset is known. Such data, however, is often unavailable due to privacy considerations or measurement limitations. In the absence of geo-referenced individual-level data, two scenarios can arise depending on what kind of geographic information is available. If researchers have information about each observation’s location within aggregate but small geographic units, a modified RD framework can be applied, where the running variable is treated as discrete instead of continuous. If researchers lack this type of information and instead only have access to the location of units within coarse aggregate geographic units that are too large to be considered in an RD framework, the available coarse geographic information can be used to create a band or buffer around the border, only including in the analysis observations that fall within this band. We characterize each scenario, and also discuss several methodological challenges that are common to all research designs based on geographically discontinuous treatment assignments. We illustrate these issues with an original geographic application that studies the effect of introducing copayments for the use of the Children’s Health Insurance Program in the United States, focusing on the border between Illinois and Wisconsin.

In this chapter, the authors will discuss the global learning partnership (GLP), which is an innovative and evidence-informed model of experiential learning for health professional students. The model is based on partnerships between universities to promote the UN Sustainable Development Goals (SDGs) agenda.

The GLP is a placement opportunity for an inter-professional cohort of visiting and local students and academics, designed as an action-orientated, community engagement learning experience focused on addressing community health and well-being needs (Goal 3). Consistent with the SDGs, strong partnerships (Goal 17) are essential to lasting impact.

The aim of developing the GLP was to design, implement, and trial a new model of global placement that was sustainable, capacity building, and a genuine learning opportunity for local and international health science students, driven by the agenda to increase learning about and action on the SDGs. This model was designed to harness the strengths of existing educational frameworks recognized for the beneficial learning opportunities they provide; inter-professional learning; learning from and with communities with SDG needs; and with a three-week immersion component for building cultural competency.

In addition to focusing on the SDG agenda, the GLP is unique in drawing on the knowledge and skills of local students and academics in educating the visiting students to work in geopolitically and socioculturally complex community setting building, as visitors learn from both local students and the local community about best practice.

This chapter will provide discussion of the theoretical framework underpinning the model design and draw on the student and academic experience. Practice details are also provided to allow educators in other organizations to replicate the partnership model.

The progress of a nation is quite closely linked with the quality of education it offers its citizens. The onus of nurturing future leaders, the students, lies significantly with higher educational institutions (HEIs) and the academic staff associated with such institutions. Therefore, HEIs need so that these engaged employees may go on to look beyond regular extrinsic motivators to ensure employee engagement to mentor students effectively. In this study, we attempted to investigate the influence of an important predictor of employee engagement, leadership, specifically servant leadership style mediated through job satisfaction. A structured questionnaire was administered to the academic staff of the top 25 universities in India. The data collected and the proposed hypotheses were tested using structural equation modeling. The results confirmed that job satisfaction mediates the relationship between servant leadership and work engagement. The study offers insights into the importance of servant leadership to foster employee engagement and thereby institutional effectiveness in the educational sector.

US Navy warships are capital-intensive national defense assets that require periodic depot and intermediate level maintenance availabilities (periods). Oftentimes, ship maintenance is deferred or forgone altogether due to geopolitical strife or fiscal challenges. The impacts of missed maintenance are not only a burden on ships’ crews, but they also have a deleterious effect on current and future readiness. It is a difficult task to strike a balance between current and future readiness when insufficient resources are available to sustain a fleet of warships. This paper draws from multi-attribute utility theory (MAUT) to develop a ship maintenance decision-making model that considers attributes from the current and life cycle readiness cohorts. Using the current maintenance plans for two DDG 51-class ships entering availabilities in same fiscal year, this model determines which ship is more capable of absorbing a loss of maintenance and planned modernizations relative to the context of the decision environment. Five attributes are considered for the overall decision: mandatory maintenance, non-mandatory maintenance, mission impact from maintenance, mission impact from planned modernizations, and maintenance backlog. The model presented here is generalizable to a number of U.S. Navy ships and watercraft and can be used to inform decision-makers of the short- and long-term impacts of deferring critical maintenance.

Considerable research has linked lower socioeconomic status (SES) and non-majority racial-ethnic status with poorer health, but much research in this area has been cross-sectional and has not examined whether psychological well-being factors help mediate the influence of SES and race-ethnicity on physical health over time. The aims of this study were to examine the degree to which education, household income, and race-ethnicity were independently associated with changes in physical health (global health, functional limitations, and mortality) over five years and to examine whether psychological well-being factors (depressive symptoms, global happiness, self-esteem, and self-efficacy) helped account for these associations. Results from models estimated using data from 7,414 midlife and older adults (4,513 women, 2,883 men) aged 35–98 who participated in the U.S. National Survey of Families and Households 1987–1993 suggested that lower education helped predict a greater decline in global health and a greater increase in functional limitations over time for both men and women; lower income helped predict a greater decrease in global health for men and a greater increase in functional limitations over time for both men and women; being African-American (vs. non-Hispanic white) helped predict a greater increase in functional limitations for women; and lower income and being African-American each independently helped predict mortality over time for men. Psychological factors only very minimally accounted for social inequalities in health; however, a positive profile on each psychological factor (net of Time 1 health and numerous sociodemographic factors) helped predict more positive health over time.