Background: The Urban Primary Health Care Project (UPHCP) was implemented by the Government of Bangladesh in response to rapid urbanization and growing inequalities in access to and quality of primary health care. The goal of the project was to improve health status of the urban poor living in city corporations and municipalities through the provision of health care services by NGOs that are contracted through public-private partnership. The first phase of the project started in 1998 and the project is currently in its fourth phase covering more urban areas than the first three phases. This study evaluates the impact of the second phase project (UPHCP-II) on health outcomes, mainly child diarrhea, acute respiratory infection, antenatal and postnatal care, skilled birth attendance, breastfeeding prevalence, contraceptive prevalence, sexually transmitted infections, and HIV/AIDS awareness. Methods: The effect of the project was estimated through propensity score matching between project and nonproject areas comparing baseline and endline surveys over a six-year period from 2006 to 2012. An innovation of this study is the recalibration of the sampling weights that allows the use of these two independent surveys in impact evaluation. Results: Over the six-year period, UPHCP-II improved the health status of the population in project areas compared to non-project areas. The study found significant improvement in health outcomes in terms of reduced diarrhea and acute respiratory infection in children, which explains the downward trend in child mortality rate. Moreover, the project also improved antenatal care and skilled birth attendance. Contraceptive prevalence and HIV/AIDS awareness and avoidance increased, and sexually transmitted infections decreased. Conclusions: UPHCP-II was effective in achieving its health outcome targets, while previous studies show that it was efficient in the delivery of health care and clients were highly satisfied because health facilities were in close proximity, and doctors and staff were perceived as responsive in delivering high quality of care. The results of this study could help inform future design and implementation of urban health interventions that involve contracting primary health care service delivery in Bangladesh and other similar settings.

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This paper looks at the spatial relationship of the average per capita income growth using provincial data from 1988 to 2009. The results from the study provide insights on the geographical dimensions of provincial income growth and showed evidence on the role of spatial effects in the formal econometric analysis of intra-country income growth models. Despite data limitations, the study provides a strong empirical evidence of the presence of positive spatial dependence or degree of similarity in the average per capita income growth of the provinces, albeit the degree of positive spatial dependence weakens in the latter periods. This positive spatial correlation suggests the provinces may be converging in terms of their income growth and they do so in movements similar to their neighbors. Moreover, the study shows that spatial dependence weakened in the latter periods (1994-2000 and 2000-2009). The weakening of spatial dependence may provide insights on the uneven provincial/regional income growth experienced in the country. One possible explanation of the weak spatial dependence is that two or more groups of neighboring provinces are growing at similar rates within the group, but at different rates across groups. This opens the possibility of having different convergence clubs (of provinces) within the country.

The establishment of the Sustainable Development Goals in 2015 entails generating relevant and timely statistics for monitoring and policymaking. The Philippine Statistics Authority (PSA) generates poverty statistics using the Family Income and Expenditure Survey (FIES). However, due to certain limitations, the FIES collection and calculation of official poverty statistics is done only every three years. In this regard, this paper presents a method of filling in the gaps by interpolating annual poverty statistics, particularly the poverty incidence, using macroeconomic indicators and demographic and employment information from the Labor Force Survey (LFS). These explanatory variables were related to the poverty incidence using the Dynamic Factor Model (DFM) in the state-space form to produce estimates for years when poverty statistics are not available. Relatively high forecast accuracy was observed for the predicted values of poverty incidence.