Modeling and Explaining County-level Prosperity in the U.S

Abstract. This paper explores the impact of space on prosperity. In order to do this, it develops a spatial model for locating prosperous counties and for identifying factors associated with prosperity. Using principal component analysis, a county-level prosperity index is created that comprises four measures: high school dropouts, housing conditions, unemployment, and poverty rates. Five categories of independent variables-demographic, economic, geographic, agricultural, and human and social capital-are used in the analysis. The spatial autocorrelation method has been used to determine the spatial pattern of prosperous counties, and the spatial econometric method has been used to develop a model that explains prosperity. The result shows that more prosperous counties have lower minority populations, more economic opportunities, and higher social and human capital. A policy reformulation is important in addressing the issues of less prosperous counties by creating jobs and enhancing social and human capital at regional levels

Implementing a Post-Project Sustainability Study (PSS) of a Development Project: Lessons Learned from Indonesia

This policy brief draws on lessons learned from a recent post-project sustainability study (PSS) of a community health project in Indonesia. Understanding how project components and results are evolved, sustained, and adopted after the conclusion of a project is important from policy perspective as the lessons learned serve to inform future programming, as well as contribute to the general body of knowledge. The paper suggests that the focus of any PSS should not only examine what activities are sustained, but also the factors responsible for sustaining the results. It also suggests that, mixed methods – quantitative and qualitative – help research teams understand why and how project activities are sustained. It further suggests that PSSs are different from traditional impact evaluations, so including all of the stakeholders in the study is crucial in order to understand how they contributed toward the project’s sustainability. Other conclusions related to sustainable development goals (SDGs) are also made

Modeling and Explaining County-level Prosperity in the U.S.

This paper explores the impact of space on prosperity. In order to do this, it develops a spatial model for locating prosperous counties and for identifying factors associated with prosperity. Using principal component analysis, a county-level prosperity index is created that comprises four measures: high school dropouts, housing conditions, unemployment, and poverty rates. Five categories of independent variables—demographic, economic, geographic, agricultural, and human and social capital—are used in the analysis. The spatial autocorrela-tion method has been used to determine the spatial pattern of prosperous counties, and the spatial econometric method has been used to develop a model that explains prosperity. The result shows that more prosperous counties have lower minority populations, more economic opportunities, and higher social and human capital. A policy reformulation is important in addressing the issues of less prosperous counties by creating jobs and enhancing social and human capital at regional levels