Pandemic, informality, and vulnerability: impact of COVID-19 on livelihoods in India
Bibliographic record
Abstract
We analyze findings from a large-scale survey of around 5000 respondents across 12 states of India, conducted during the months of April and May 2020, to study the impact of COVID-19 pandemic containment measures (lockdown) on employment, livelihoods, and food security. Given the predominantly informal nature of employment and critically low investment in State-funded social security nets, the impact, albeit unprecedented in its scale, was not entirely unexpected in its nature. We find that around two-thirds of respondents reported losing employment during the lockdown, and those that continued to be employed witness a sharp decline in earning. Further, with critically low levels of social security net, the loss in employment quickly translated into food and livelihoods insecurity. Almost 80 per cent of households experienced a reduction in food intake, more than 60 per cent did not have enough money for a week’s worth of essentials, and a third took a loan to cover expenses during the lockdown. We also use a set of logistic regressions to identify how employment loss and reduction in food intake varied with individual and household-level characteristics. Based on our analysis, we argue that while there is an urgent need to undertake effective measures to support livelihoods and facilitate an economic recovery, we also highlight the necessity to critically evaluate the current development trajectory, whereby decades-long high economic growth has failed to translate into more secure livelihoods for a vast majority of the workforce.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".