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Climate vulnerability and household nutrition in India

Table 3. District-wise climate vulnerability and household nutrition status in Odisha, India.
Districts Rank Rank category 0‑0.106 = Low, Weight‑for‑height (wasting) % of Children 6‑59 % of women
0.115‑0.170 = Medium, 0. months who are having 15‑49 years who are
171‑0.193 = High anemic having anemic
Mayurbhanj 1 Low 17.2 34.5 42.4
Ganjam 2 Low 16.4 37.4 41.3
Sundargarh 3 Low 31.4 75.3 71.4
Angul 4 Medium 21.6 37.4 44.0
Keonjhar 5 Medium 19.0 32.7 40.5
Kandhamal 6 Medium 23.1 42.7 52.7
Sambalpur 7 Medium 22.3 75.0 69.2
Khordha 8 Medium 13.8 19.0 45.3
Cuttack 9 Medium 9.1 18.9 37.8
Koraput 10 Medium 28.5 71.4 63.3
Kalahandi 11 Medium 24.8 67.2 68.7
Dhenkanal 12 Medium 19.0 39.4 39.4
Jharsuguda 13 Medium 24.8 67.1 69.2
Bolangir 14 Medium 26.1 67.3 61.1
Rayagada 15 Medium 23.1 49.8 55.4
Gajapati 16 Medium 18.4 57.9 58.5
Jajpur 17 Medium 9.1 18.9 37.8
Jagatsinghpur 18 Medium 12.6 23.4 35.8
Balasore 19 Medium 18.0 28.6 41.1
Nuapada 20 High 26.4 63.9 64.0
Nabarangpur 21 High 36.0 71.9 71.5
Nayagarh 22 High 17.5 26.5 39.8
Deogarh 23 High 19.9 30.0 42.6
Malkangiri 24 High 32.5 72.2 71.3
Baragarh 25 High 24.2 68.8 68.8
Kendrapara 26 High 12.3 28.7 42.3
Puri 27 High 12.1 29.2 44.3
Boudh 28 High 22.5 44.1 49.9
Sonepur 29 High 22.3 75.0 69.2
Bhadrak 30 High 15.3 22.7 43.5
Source: Computed by authors using data from secondary sources
matrix have been presented in Table 4.
The results of multiple regression analysis have been documented in Table 5. As mentioned above for better
comprehension, under each dependent variable, two separate models were created. First is one to one matching of
the composite values with dependent variables, and the second one is the matching of independent variables with all
associating factors except composite values. In this process, six models were made to run for three identified predictors
which relate to household nutrition outcomes. The results of the multivariate analysis suggest that household nutrition
status, particularly child nutrition indicators (wasting and childhood anemia), are associated significantly with mother’s
anemic status, literacy level, and household’s economic and social caste composition. The child nutrition status relates
not only to socioeconomic factors but also to biophysical factors such as district’s predisposition to gross cropped area,
percentage of the area having forest coverage, annual rainfall, and irrigation facility.
Similarly, the study also finds that household women nutrition status is also influenced by household’s socioeconomic
attributes such as women’s social category, level of urbanization, and biophysical factors such as cropping intensity. Ironically,

48 International Journal of Population Studies | 2020, Volume 6, Issue 1