Accountability Initiative, Centre for Policy Research, New Delhi, India
Accountability Initiative, Centre for Policy Research, New Delhi, India.
BMJ Glob Health. 2021 Apr;6(4). doi: 10.1136/bmjgh-2020-004705.
For investments to translate into improved public service delivery, having a strong public finance management (PFM) system that lays out the rules, institutions and processes by which public funds are managed is critical. To enable a better understanding of the nutrition financial landscape, this paper seeks to determine whether the current PFM system in India allows for capturing required nutrition data. It does this by mapping the availability and comparability of data for a set of key nutrition-specific interventions through the budget cycle: from budget formulation, to execution, and finally, evaluation. The study finds significant gaps in data availability including absence of financial data by level of governance, geography and intervention components. These challenges relate to gaps in PFM design in India from weak planning processes, line-item budgeting, unavailability of time costs, inefficient fund release processes, difficulties in estimating target populations and the lack of output costing. These gaps in the PFM system and consequent data issues have several implications which may lead to strained delivery. This in turn impacts quality and the possibility of course correction. Some of these challenges can be overcome by ensuring planning processes are enforced, expanding existing data systems, making more data available in the public domain, using existing research better and using assumptions carefully to cover data gaps.
为了使投资能够转化为改善公共服务的交付,拥有一个强大的公共财政管理(PFM)系统至关重要,该系统规定了管理公共资金的规则、机构和流程。为了更好地了解营养财务状况,本文试图确定印度现行的 PFM 系统是否允许获取所需的营养数据。为此,它通过在预算周期内映射一组关键营养特定干预措施的数据的可用性和可比性来做到这一点:从预算编制到执行,最后是评估。研究发现数据可用性存在重大差距,包括缺乏按治理级别、地理和干预组件划分的财务数据。这些挑战与印度 PFM 设计中的差距有关,包括规划流程薄弱、项目预算、时间成本不可用、资金发放流程效率低下、估计目标人群困难以及缺乏产出成本核算。PFM 系统中的这些差距以及随之而来的数据问题有几个影响,可能导致交付紧张。这反过来又会影响质量和纠正的可能性。通过确保实施规划流程、扩大现有数据系统、在公共领域提供更多数据、更好地利用现有研究以及谨慎使用假设来填补数据空白,可以克服其中一些挑战。