Impacts of NEWMAP Intervention on Sustainable Development of South East Nigeria

This dataset contains household-level panel data used to analyze the impact of environmental interventions on clean energy adoption in Nigeria. It supports the study titled “Environmental Intervention and Household Clean Energy Adoption: Assessing the Impact of Nigeria’s NEWMAP on Cooking and Lighting Energy Use.” The dataset enables examination of how participation in the Nigeria Erosion and Watershed Management Project (NEWMAP) influences household transitions to cleaner cooking and lighting energy sources.
The data originate from household surveys conducted in communities participating in the NEWMAP program, a World Bank-supported environmental intervention designed to address land degradation, erosion, and watershed management in Nigeria. Surveys were administered at two time points, baseline (pre-intervention) and endline (post-intervention) allowing for a quasi-experimental evaluation of program impacts.
The dataset is structured as a balanced household panel. The unit of observation is the household, tracked across two periods (baseline and endline). The final analytical sample consists of 364 households observed in both rounds, yielding 728 total observations. Households are uniquely identified using a consistent identifier across survey waves.

The dataset includes several categories of variables. Outcome variables capture household energy use behavior: clean cooking adoption (equal to 1 if the household primarily uses LPG or electricity for cooking, and 0 otherwise) and clean lighting adoption (equal to 1 if the household uses electricity or solar energy for lighting, and 0 otherwise).

Key explanatory variables include a treatment indicator identifying households located in areas that received NEWMAP physical environmental interventions, and a post-intervention indicator distinguishing endline from baseline observations. Their interaction term enables estimation of causal effects using a difference-in-differences framework.

The dataset also contains a rich set of control variables. Socioeconomic and demographic variables include age, gender, education, literacy status, household size, and employment status. Economic indicators include household income, total expenditure, housing ownership, and housing characteristics. Infrastructure and access variables include electricity access, distance to water sources, and market access. Geographic variables classify households as rural, peri-urban, or urban and include community or watershed identifiers.

Data were collected using structured questionnaires administered by trained enumerators following standardized survey protocols. Data processing involved cleaning and validation of responses, harmonization of variables across survey rounds, construction of derived variables (including clean energy indicators), and merging of baseline and endline datasets using household identifiers. Observations with missing or inconsistent identifiers were excluded to ensure data quality and consistency.