In recent times, Bihar, the poorest state of India, has emerged as one of the fastestgrowing states in India, witnessing a substantial fall in poverty in the post-reform era. On the other hand, despite recent efforts to improve basic service availability, this state still lags behind the national average or international standards. It is also not distributed evenly over space and among individuals. In the context of the availability of basic services, following the vertical equity principle, it is expected that the poor should be provided with a higher level of basic services than the non-poor. In this paper, we attempt to find out how poverty varies across different spatial categories, i.e., rural, slum and non-slum within urban areas in Bihar. Secondly, how do the three spatial categories differ in the availability of a few select (publicly provided) basic services, namely access to drinking water, latrine, garbage cleaning facilities by local bodies and all-weather roads? Lastly, this paper tries to understand the association between poverty and the availability of these basic services. We included other less developed Empowered Action Group (EAG) states in the analysis for a comparative assessment of Bihar. This study used the 78th Multiple Indicator Survey Data (2020) conducted by the National Sample Survey (NSS). The Tendulkar Methodology of poverty estimation has been used in this paper. Our findings show that rural and slum areas have a similar level of poverty in Bihar. We have developed a set of Binary Logistic Models to examine the association between poverty and residential categories (rural, slum, and non-slum). The Binary Logistic Models show that slum dwellers are more likely to be poor than rural people in Bihar. Results on the availability of basic services show that rural people have much lower access to all the above-mentioned basic services than slums (except for all-weather road availability). The poor across all these spatial categories receive considerably lower access to basic services in Bihar. The rural poor receive the lowest availability of basic services. Binary Logistic Models further strengthen iniquitous access to basic services.