Association with SocioEconomic Indicators
Poverty affects nutrition (Nelson 2000) leading to undernutrition in the poor population. Stunting was the most dominant phenomenon among children under five years. So the percentage of stunting was associated with socioeconomic factors like percentage of population below poverty line, per capita GDP, and growth rate of the state. Undernutrition is a result of poor dietary intake, poor maternal health combined with lack of safe water and sanitation along with poor health services (UNICEF 1998). Undernutrition is responsible for poor mental health (Martins et al. 2011), higher vulnerability to ill health, and a reduced physical work capacity (Non et al. 2016), which is making the workforce inefficient and thus posing a problem in the economic development of nations. Further, under nutrition which is caused by poverty also leads back to undernutrition which again causes poverty and the cycle continues. The marginal propensity to consume is higher for poor people because they spend higher proportion of their income in consumption. Thus, decline in income will hamper the consumption expenditure. Bivariate analysis was done to work out relationship between prevalence of stunting and each economic factor under consideration. The association between the percentage of stunted children and the percentage of the population below poverty line is strong and positive (Figure 1.4a). This is anticipated because poverty leads to insufficient food intake, less prenatal care, child malnutrition, and unhealthy diet. A few states however deviate from the predicted line. Meghalaya, Rajasthan, and Gujarat are clear negative outliers with a much higher percentage of stunted children as compared to their poverty level. Goa, Kerala, Manipur, and Arunachal Pradesh, on the other hand, are positive deviants, i.e. they have lower percentage of stunting in comparison to the level of poverty. Improved drinking water sources, improved sanitation facility, use of iodized salt, literacy among women, antenatal care, and anemia among children and women also impact the level of undernutrition in India (Ghosh 2020).
Table 1.2 Average Annual Rate of Return of stunting and wasting from 2005–2006 to 2015–2016.
Source: Calculated by author from data obtained from NFHS 4.
| State | AARR in stunting | AARR in wasting |
|---|---|---|
| India | 2.21 | −0.59 |
| Arunachal Pradesh | 3.80 | −1.24 |
| Assam | 2.42 | −2.18 |
| Bihar | 1.40 | 2.61 |
| Chhattisgarh | 3.36 | −1.71 |
| Delhi(NCT) | 2.76 | −0.32 |
| Goa | 2.39 | −4.50 |
| Gujarat | 2.90 | −3.51 |
| Haryana | 2.91 | −1.05 |
| Himachal Pradesh | 3.76 | 3.37 |
| Jammu Kashmir | 2.42 | 1.99 |
| Jharkhand | 0.94 | 1.07 |
| Karnataka | 1.87 | −4.02 |
| Kerala | 2.16 | 0.13 |
| Madhya Pradesh | 1.73 | 3.00 |
| Maharashtra | 2.93 | −4.49 |
| Manipur | 2.06 | 2.76 |
| Meghalaya | 2.27 | 6.73 |
| Mizoram | 3.42 | 3.81 |
| Nagaland | 3.00 | 1.62 |
| Odisha | 2.74 | −0.40 |
| Punjab | 3.50 | −5.42 |
| Rajasthan | 1.11 | −1.21 |
| Sikkim | 2.54 | −3.88 |
| Tamil Nadu | 1.30 | 1.19 |
| Tripura | 3.77 | 3.74 |
| Uttar Pradesh | 2.02 | −1.92 |
| Uttarakhand | 2.78 | −0.37 |
| West Bengal | 3.12 | −1.85 |
A scatterplot of the percentage of stunting and net state domestic product (NSDP) per capita, with the latter serving as a proxy for each state's per capita income in Figure 1.4b. In this case, the two variables show a negative association, with poorer states having a significantly higher percentage of stunting as compared with more prosperous states. The association though has a number of outliers. For instance, Cluster 1‐ Bihar, Uttar Pradesh, Jharkhand, Meghalaya, Uttar Pradesh, and Madhya Pradesh have much higher level of stunting as expected from states of their income level. While states like Manipur, Mizoram, Tripura, and Jammu Kashmir are positive deviants with much lower percentage of stunting as compared with states with similar income level. These are states with better sanitation, literacy, and care for pregnant women. This indicates that not only income but also other socioeconomic factors might also be major contributors. The association between the percentage of stunting and the rate of economic growth for each state is shown in Figure 1.4c, which shows not much strong relationship between the two variables. Meghalaya with negative growth rate in 2014–2015 has a high level of stunting 42% but so did states like Jharkhand, Bihar, Uttar Pradesh, and Madhya Pradesh with much higher growth rate as compared to Meghalaya. Goa and Mizoram have a much higher growth rate but the percentage of stunting is not low in these states. This gives a conclusion that growth rate of a state is weakly associated with states prevalence of malnutrition. Thus, it can be concluded that despite economic progress, India has to struggle