Dataset on Palliative Economics and Persistent Poverty in Sub-Saharan Africa
Description
Title of Dataset Dataset on Palliatives Economics and Persistent Poverty in Sub Saharan Africa Abstract This dataset compiles macroeconomic, fiscal, and social indicators relevant to the study of debt financed palliative measures and poverty persistence in Nigeria. It integrates official statistics from the Debt Management Office (DMO), World Bank’s World Development Indicators, and the Council on Foreign Relations’ Nigeria Security Tracker. The dataset is designed to support econometric analysis, particularly Two Stage Least Squares (2SLS) regression, to examine the relationship between debt servicing, governance inefficiencies, and poverty outcomes. Data Sources 1. Debt Management Office Nigeria o Nigeria Public Debt Statistical Bulletin, Q4 2023 o Provides quarterly data on external and domestic debt stock, debt servicing obligations, and fiscal structure. 2. World Bank Open Data – World Development Indicators (2024) o GDP, poverty headcount ratio, education expenditure, healthcare expenditure, and demographic indicators. o Regional aggregates for Sub Saharan Africa used for comparative analysis. 3. Council on Foreign Relations – Nigeria Security Tracker (updated July 1, 2023) o Weekly catalog of violent incidents linked to political, economic, and social grievances. o Used to contextualize governance challenges and insecurity’s impact on poverty persistence. ________________________________________ Data Coverage • Temporal Scope: 2010–2023 • Geographic Scope: Nigeria, with comparative references to Sub Saharan Africa regional aggregates. • Variables: o Debt stock (external, domestic) o Debt servicing costs o GDP (annual, per capita) o Poverty headcount ratio o Education and healthcare expenditure (% of GDP) o Violent incidents (frequency, type, actors involved) ________________________________________ Methodology • Stylized Facts: Descriptive statistics and trend analysis of debt and poverty indicators. • Econometric Analysis: Two Stage Least Squares (2SLS) regression to estimate the impact of debt servicing and governance inefficiencies on poverty outcomes. • Validation: Cross checked with multiple sources (DMO, World Bank, CFR) to ensure reliability. ________________________________________ Value of the Data • Enables replication of the study’s findings on debt poverty dynamics. • Provides policymakers with evidence on fiscal diversion from social sectors. • Supports comparative research across Sub Saharan Africa. • Useful for scholars in economics, finance, development studies, and governance. ________________________________________ Limitations • Security Tracker data ends July 2023; subsequent incidents are not included. • Some fiscal data may be subject to revisions by the DMO. • Poverty measures rely on World Bank estimates, which may differ from national statistics. ________________________________________
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Steps to reproduce
1. Data Collection o Download quarterly debt statistics from the Nigeria Public Debt Statistical Bulletin, Q4 2023 published by the Debt Management Office (DMO) via https://www.dmo.gov.ng/publications (dmo.gov.ng in Bing). o Retrieve macroeconomic and social indicators (GDP, poverty headcount, education and health expenditure) from the World Development Indicators 2024 dataset available at https://data.worldbank.org. o Access violent incident data from the Nigeria Security Tracker (Council on Foreign Relations, last updated July 1, 2023) via https://www.cfr.org/articles/p29483. 2. Data Preparation o Convert all datasets into a consistent format (CSV or Excel). o Align temporal coverage to 2010–2023. o Standardize variable names and units (e.g., GDP in constant USD, debt servicing as % of GDP, poverty headcount ratio as % of population). o Merge datasets by year to create a unified panel. 3. Descriptive Analysis o Generate stylized facts: trend lines for debt stock, debt servicing, poverty headcount, and social expenditure. o Summarize violent incidents annually to contextualize governance challenges. 4. Econometric Modeling o Apply Two Stage Least Squares (2SLS) regression. o First stage: regress debt servicing and governance variables on instrumental variables (e.g., lagged debt, external shocks). o Second stage: regress poverty headcount on predicted values from the first stage, controlling for GDP, education, and health expenditure. o Validate model assumptions (endogeneity, instrument relevance, heteroskedasticity). 5. Replication Outputs o Report regression coefficients, R², and significance levels. o Provide tables and figures showing debt poverty dynamics. o Document code (e.g., R or Stata scripts) used for analysis. 6. Limitations and Notes o Security Tracker data ends July 2023; subsequent incidents are not included. o DMO debt data may be revised; users should check for updates. o Poverty measures rely on World Bank estimates, which may differ from national statistics.