Collect General Government Spending Data based on COFOG Function classification.

Details

Mission and objectives

UN Women is the United Nations entity dedicated to gender equality and the empowerment of women. A global champion for women and girls, UN Women was established to accelerate progress on meeting their needs worldwide. UN Women supports UN Member States as they set global standards for achieving gender equality, and works with governments and civil society to design laws, policies, programmes and services needed to ensure that the standards are effectively implemented and truly benefit women and girls worldwide. It works globally to make the vision of the Sustainable Development Goals a reality for women and girls and stands behind women’s equal participation in all aspects of life, focusing on four strategic priorities: Women lead, participate in and benefit equally from governance systems Women have income security, decent work and economic autonomy All women and girls live a life free from all forms of violence Women and girls contribute to and have greater influence in building sustainable peace and resilience, and benefit equally from the prevention of natural disasters and conflicts and humanitarian action UN Women also coordinates and promotes the UN system’s work in advancing gender equality, and in all deliberations and agreements linked to the 2030 Agenda. The entity works to position gender equality as fundamental to the Sustainable Development Goals, and a more inclusive world.

Context

We expect the Online Volunteers’ contributions through this task to support our organization’s efforts towards compiling a costing SDGs model. The model is a stochastic frontier model that uses translog functions, where the input data are government spending and efficiency term that covers FDI, ODA, and debts. The model will be run as panel data covering all countries from 2000 to 2022. The data collected by the Online Volunteers will be used as input for the model, which will help in understanding the relationships between government spending, efficiency terms, and the impact they have on achieving the SDGs. This will ultimately support our efforts towards achieving the SDGs.

Task description

For the collection of data on government finance statistics, in particular, general government spending, based on the classification of COFOG functions, we seek support from 5 Online Volunteers, under the guidance of Nour Barnat, chief of the methodology unit at UNCTAD statistics in the context of the SDGs costing project for the UN summit on SDGs progress in 2023, in particular, volunteers will: - Discuss and define the objective and data collection methodology for compiling costing SDGs model, specifically related to government spending and other related variables - Develop/identify a methodology tool for data collection, if required, and propose an implementation plan. - Identify and screen for data sources related to government spending, FDI, ODA, and debts. Source of data can be international organisation, national agencies such as NSOs and ministries, as well as official government annual/quarterly/monthly reports and publications. The national sources can provide information in official languages only. Please refer to the section language to determine the language needed for these tasks. - Conduct data collection using the defined methodology and tools from selected sources. - Compare data between different data sources and fill the gaps by merging consistent data from different data sources. - Double-check existing data and validate the final datasets to be used. - Create/prepare a report capturing findings related to government spending, based on the classification of COFOG functions, for use in the stochastic frontier model. - Prepare R reports or other tools reports that automatise the data collection for easy future updates. T The Online Volunteers will be provided with other collected data and will be requested to fill the gaps and to update on new releases. The volunteers will be expected to keep all the data and supporting files provided confidential.