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Record W3124898630

Political Reservations, Access to Water and Welfare Outcomes: Evidence from Indian Villages

2011· preprint· en· W3124898630 on OpenAlexfundno aff
Raghbendra Jha, Sharmistha Nag, Hari K. Nagarajan

Bibliographic record

VenueANU Open Research (Australian National University) · 2011
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsWelfareProductivityLabour economicsCorporate governanceEconomicsWork (physics)WageDistribution (mathematics)PoliticsDemographic economicsBusinessEconomic growthPolitical scienceMarket economy
DOInot available

Abstract

fetched live from OpenAlex

In a developing economy with an ethnically diverse society, such as India's, household welfare and its distribution within the household unambiguously depend on how much time each member of the household spends on productive activity. In this paper we examine the welfare impact of reducing the time spent by members of households, particularly women, through political reservations in rural India. Using a unique data set we find that (i) Political reservations and the ability of women to participate in the process of governance contribute to household welfare by allowing women to participate in labor markets, essentially because provision of public goods and in particular water, increases the productivity of household labor time. (ii) The concomitant decline in household work and increase in labor market participation is a robust indicator of increased productivity of household labor time being translated into productive work. In particular women participate in self employment and on cultivation. The effect on household incomes caused by members engaged in self-employment activities and own-cultivation is higher compared to effects caused by participation in off-farm wage labor. (iii) Further, our results are robust to the inclusion of residential location, access to credit, and shocks.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0030.009
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.433
GPT teacher head0.405
Teacher spread0.027 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2011
Admission routes1
Has abstractyes

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