Debt Financed Palliatives and Poverty in Sub Saharan Africa: Nigerian Data
Description
Data Description Research Hypothesis The central hypothesis is that debt financed palliative interventions, while intended to provide short term relief, exacerbate poverty persistence in Nigeria by constraining fiscal space and diverting resources from productive sectors. Endogeneity concerns, where fiscal space is simultaneously influenced by debt service and poverty outcomes, necessitate the use of a Two Stage Least Squares (2SLS) framework to establish causal relationships. Data Overview The dataset spans 2010–2023 and integrates macroeconomic, fiscal, and social indicators from the Central Bank of Nigeria (CBN), National Bureau of Statistics (NBS), World Bank, and the Council on Foreign Relations’ Nigeria Security Tracker. Key variables include debt service (DEBTSERV), fiscal space (FISPACE), exchange rate (EXCHRATE), unemployment (UNEMPLOY), monetary policy rate (MOPORATE), insecurity related fatalities (INSECURITY), Gini index (GINDEX), maximum lending rate (MAXRATE), social expenditure (SOCEXP), poverty headcount (POVERTY), inflation (INFLATION), and National Poverty Index (NPI). Instruments used in the 2SLS estimation include oil price (OILPRICE), industry growth (INDGROWTH), GDP growth (GDPGROWTH), lagged poverty and inflation, NPI, and climate indicators. Methodology Stage 1 instruments fiscal space (FISPACE) using exogenous variables to generate predicted values. Stage 2 uses these predicted values to estimate their effect on poverty, controlling for macroeconomic and social indicators. This approach mitigates reverse causality and unobserved confounders, ensuring consistent and unbiased estimates. Findings The data show that rising debt service obligations significantly reduce fiscal space, limiting government capacity to invest in education, healthcare, and infrastructure. The 2SLS results confirm that constrained fiscal space is positively associated with poverty persistence, while insecurity and inequality further amplify adverse outcomes. Social expenditure has a mitigating effect but is insufficient when debt servicing dominates fiscal priorities. Overall, the dataset explains a large share of poverty variability, highlighting the structural link between debt dependence and entrenched poverty. Interpretation and Use Researchers can replicate the econometric analysis to test debt poverty dynamics in Nigeria or extend it to other Sub Saharan African countries with similar economic structures. Policymakers can use the dataset to evaluate trade offs between debt servicing and social investment, informing strategies aligned with Agenda 2063 and the Sustainable Development Goals (SDGs). The dataset is particularly valuable for comparative studies in development economics, fiscal policy, and governance. Limitations Security Tracker data ends July 2023; fiscal statistics may be revised by CBN or NBS; poverty measures rely on World Bank estimates, which may differ from national figures.
Files
Steps to reproduce
Replication Workflow Diagram Step 1: Data Collection • Gather debt service, fiscal space, exchange rate, monetary policy rate, maximum lending rate, and social expenditure from the Central Bank of Nigeria (CBN). • Obtain inflation and National Poverty Index from the National Bureau of Statistics (NBS). • Retrieve poverty headcount, unemployment, Gini index, and climate indicators from the World Bank Open Data. • Collect insecurity related fatalities from the Council on Foreign Relations’ Nigeria Security Tracker. Step 2: Data Preparation • Convert all datasets into a consistent format (CSV/Excel). • Align variables to a common time frame (2010–2023). • Standardize units (e.g., GDP in constant USD, ratios as percentages). • Merge datasets by year to create a unified panel. Step 3: Variable Definition • Dependent Variable: Poverty headcount ratio (POVERTY). • Endogenous Variable: Fiscal space (FISPACE). • Instruments: Oil price, debt service, industry growth, GDP growth, lagged poverty, lagged inflation, NPI, climate, maximum lending rate, Gini index. • Controls: Exchange rate, unemployment, monetary policy rate, insecurity, social expenditure. Step 4: Stage 1 Regression (Instrumenting FISPACE) • Run regression of FISPACE on instruments. • Generate predicted values of fiscal space ((\hat{FISPACE})). • Test instrument validity (relevance and exogeneity). Step 5: Stage 2 Regression (Estimating Poverty Outcomes) • Regress poverty headcount (POVERTY) on predicted fiscal space ((\hat{FISPACE})) and control variables. • Interpret coefficients: o Positive relationship between debt service/fiscal constraints and poverty persistence. o Negative relationship between social expenditure and poverty persistence. o Insecurity and inequality amplify poverty outcomes. Step 6: Robustness Checks • Conduct weak instrument tests. • Run alternative specifications (lagged variables, different controls). • Compare results across sub samples or alternative poverty measures. Step 7: Interpretation and Application • Confirm hypothesis: debt financed palliatives constrain fiscal space and sustain poverty. • Use findings to inform policy trade offs between debt servicing and social investment. • Extend methodology to other Sub Saharan African countries for comparative analysis.