Labour Force Participation of Military Spouses: Global and Indian Perspective
Bibliographic record
Abstract
Military spouses encounter exceptional challenges in employment due to the frequent relocations and deployments inherent to military life.This review examines the labour force participation outcomes of military spouses globally and in India.Studies across Western countries reveal that military spouses have significantly lower employment rates compared to their civilian counterparts, with gaps ranging from 10-15 percentage points.Frequent relocations disrupt career progression, while deployments increase caregiving responsibilities, hindering consistent workforce engagement.Underemployment, where spouses work in jobs below their qualifications, is another pervasive issue.Research in the US, UK, Canada, and Australia shows military spouses experience higher rates of underemployment, often taking lower-skilled roles due to relocation constraints.This underemployment results in lower earnings and stalled career mobility.Policies have been implemented by some nations to support military spouse employment, such as career counselling, job portals, and private sector partnerships.However, more systemic interventions are recommended, including remote work opportunities, credential portability, and subsidized childcare.In India, cultural norms and structural barriers further restrict labour force participation among military spouses.Only 18-26% of Indian military spouses are formally employed, with most engaged in informal home-based work.Geographic isolation of military bases, lack of public transit, and credential transfer issues exacerbate employment challenges.While local initiatives like vocational training and entrepreneurship guidance exist, centralized national policies are lacking.Experts recommend interventions tailored for the Indian context, such as standardized licensing frameworks, remote work opportunities, small business support, and public-private partnerships to leverage this underutilized talent pool.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".