Deciphering Disparities in Childhood Stunting in an Underdeveloped State of India: An Investigation Applying the Unconditional Quantile Regression Method Article Swipe
Saswata Ghosh
,
Santosh Kumar Sharma
,
Debarshi Bhattacharya
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-50613/v2
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-50613/v2
Background Unacceptably high rate of childhood stunting for decades remained a puzzle in the eastern Indian state of Bihar. Despite various programmatic interventions, nearly half of the under-five children (numerically about 10 million) are still stunted in this resource-constrained state. Data and Methods Using four successive rounds of National Family Health Survey (NFHS) data spread over more than two decades and by employing quantile regressions and counterfactual decomposition (QR-CD), the present study aims to assess effects of various endowments as well as returns to those endowments in disparities in childhood stunting over the period. Results The results show that although the child’s height-for-age Z-scores (HAZ) disparity largely accounted for differing levels of endowments during the earlier decades, in the later periods, inadequate access to the benefits from various development programmes was also found responsible for HAZ disparities. Moreover, effects of endowments and their returns varied across quantiles. We argue that apart from equalizing endowments, ensuring adequate access to different nutrition-centric programmes is essential to lessen the burden of childhood stunting. Conclusion The state must focus on intersectoral convergence of different schemes in the form of state nutrition mission, and, strengthen nutrition-centric policy processes and their political underpinnings to harness better dividend.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-50613/v2
- https://www.researchsquare.com/article/rs-50613/v2.pdf?c=1600384543000
- OA Status
- green
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3199828986
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https://openalex.org/W3199828986Canonical identifier for this work in OpenAlex
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https://doi.org/10.21203/rs.3.rs-50613/v2Digital Object Identifier
- Title
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Deciphering Disparities in Childhood Stunting in an Underdeveloped State of India: An Investigation Applying the Unconditional Quantile Regression MethodWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-09-17Full publication date if available
- Authors
-
Saswata Ghosh, Santosh Kumar Sharma, Debarshi BhattacharyaList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-50613/v2Publisher landing page
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https://www.researchsquare.com/article/rs-50613/v2.pdf?c=1600384543000Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.researchsquare.com/article/rs-50613/v2.pdf?c=1600384543000Direct OA link when available
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Quantile regression, Quantile, Econometrics, State (computer science), Environmental health, Geography, Statistics, Economics, Medicine, Mathematics, AlgorithmTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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36Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.lessen | 165 |
| abstract_inverted_index.levels | 111 |
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| abstract_inverted_index.spread | 55 |
| abstract_inverted_index.state. | 40 |
| abstract_inverted_index.varied | 145 |
| abstract_inverted_index.Despite | 20 |
| abstract_inverted_index.Methods | 43 |
| abstract_inverted_index.Results | 95 |
| abstract_inverted_index.decades | 9, 60 |
| abstract_inverted_index.earlier | 116 |
| abstract_inverted_index.eastern | 15 |
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| abstract_inverted_index.harness | 199 |
| abstract_inverted_index.largely | 107 |
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| abstract_inverted_index.returns | 83, 144 |
| abstract_inverted_index.schemes | 181 |
| abstract_inverted_index.stunted | 36 |
| abstract_inverted_index.various | 21, 78, 128 |
| abstract_inverted_index.(QR-CD), | 69 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.National | 49 |
| abstract_inverted_index.Z-scores | 104 |
| abstract_inverted_index.adequate | 156 |
| abstract_inverted_index.although | 100 |
| abstract_inverted_index.benefits | 126 |
| abstract_inverted_index.children | 29 |
| abstract_inverted_index.decades, | 117 |
| abstract_inverted_index.ensuring | 155 |
| abstract_inverted_index.million) | 33 |
| abstract_inverted_index.mission, | 188 |
| abstract_inverted_index.periods, | 121 |
| abstract_inverted_index.quantile | 64 |
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| abstract_inverted_index.stunting | 7, 91 |
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| abstract_inverted_index.accounted | 108 |
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| abstract_inverted_index.child’s | 102 |
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| abstract_inverted_index.differing | 110 |
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| abstract_inverted_index.political | 196 |
| abstract_inverted_index.processes | 193 |
| abstract_inverted_index.stunting. | 170 |
| abstract_inverted_index.Background | 1 |
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| abstract_inverted_index.counterfactual | 67 |
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| abstract_inverted_index.resource-constrained | 39 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.24911525 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |