Yours, mine, and ours: gender, intra-household dynamics, and financing solar home systems in Tanzania
Bibliographic record
Abstract
Abstract The financing of off-grid solar is a crucial component for the expansion of electricity access, particularly across sub-Saharan Africa. Emerging literature in energy access research analyzes the role of gender and inequalities in access and subsequent outcomes; however, beyond gendered outcomes, the field has yet to interrogate the intra-household dynamics of obtaining access. The literatures of the intra-household dynamics of budgeting and energy access have remained distinct. Bridging these literatures, we present the first analysis of if and how intra-household dynamics relate to how individuals obtain energy access through our specific case of financing solar home systems (SHSs). Drawing on 113 interviews across four districts in Tanzania, we employ qualitative thematic analysis and quantitative generalized linear model-based prevalence ratio estimates. We find five categories of how SHS adopters describe gender and intra-household dynamics relating to the financing of their SHS. We find it inappropriate to treat the household as a single entity as in half our sample, women are either solely or jointly responsible for financing the SHS. Female headed households have a higher prevalence of relying on debt to finance their SHS, while households with female financial decision-making power have a higher prevalence of relying on savings. The unitary model of a household limits the sector’s ability to efficiently support multi-adult households as they navigate individual needs and preferences in the financing and ultimate acquisition of the SHS. Researchers, policy makers, and the private sector could further acknowledge and explore intra-household dynamics and consider shifting the focus away from debt-based financing towards energy-dedicated savings accounts for female customers and improving borrowing conditions for female headed households.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".