Pandemic, informality, and vulnerability: impact of COVID-19 on livelihoods in India
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it