This paper proposes an approach to tracking poverty-reducing public spending under the Enhanced HIPC Initiative and suggests a way forward to strengthen the expenditure management capacity of HIPCs in both the short and medium terms.
This paper focuses on a specific area of health financing, the allocation of public resources, and the extent to which different approaches enable poor people to access essential services.
This paper discusses issues that arise in trying to assess the impact of fiscal policy on
poverty, considering this question within a country, rather than on cross-country
evidence.
Benefit incidence analysis is widely used to assess the distributional impact of public spending. This article examines whether this now-standard methodology provides a reliable guide to the distributional impact of public spending reforms.
This practitioner's guide outlines the basic methodology of benefit incidence analysis, its recent applications highlighting different variants of the approach, and types of data manipulation which can be helpful for policy.
The concept of 'budgets as if people mattered' is inspired by a large number of initiatives that have emerged around the world during the last fifteen years to examine public budgets through a poverty or gender lens. The paper begins by laying out a contextual framework and briefly reviewing progress to date on the commitments made in Copenhagen and Beijing.
This paper critically examines how aid dependent low-income countries have approached the process of public expenditure management reform during the 1990s.
This paper argues that budgetary allocations can be quite misleading in explaining outcomes and making policy decisions in a weak institutional context, particularly in Africa.
When setting spending priorities in education and health, countries all too often target expensive schemes which can be shown only to benefit specific sections of the population. This book pleads for a series of policy orientations leading towards pro-poor health and education spending.