The statistical distribution of sample households across occupational groups is presented in table 1 and across socioeconomic correlates is presented in supplementary table S1. The average per capita calorie intake is very similar across rural (2172 kcal) and urban (2163 kcal) India (Table 2). In rural areas, households under high level services and managerial professions (particularly science, life science and health professionals) report the highest average per capita calorie intake (in excess of 2300 kcal). The lowest calorie intake (2086 kcal) is estimated for agricultural and fishery labor households. In urban areas, the similar group of professionals and managers has highest levels of caloric intake (in excess of 2400 kcal) whereas households belonging to low-end workers and laborers report low intake (below 2100 kcal). It is worth noting that across both rural and urban areas, market oriented skilled agricultural and fishery workers’ (2261 kcal – rural, 2165 kcal - urban) have higher calorie intakes than those belonging to subsistence agricultural (2165 kcal – rural, 2149 kcal - urban) and fishery workers or agricultural and fishery laborer (2086 kcal – rural, 2071 kcal - urban). Nevertheless, across both rural and urban areas, the calorie intake has a wider distribution around the mean and can be confirmed by glancing through the boxplots presented in Figure 1 or at the standard deviations reported in Table 2.
Further, Table 2 reports the percentage of households with insufficient per capita calorie intake which is 33.8% in rural India and 18.5% in urban India. This percentage varied significantly across occupational groups. In rural India, the highest level of insufficiencies is noted among workers in precision, handicraft, printing and related trades (45.2% households). However, in the urban areas, only 20.3% of households of same occupational group were reported to receive inadequate caloric intake. Such huge rural-urban divide in numbers perhaps display the intricacies related to market size and demand, value addition and remuneration gaps. In addition to this, factors such as accessibility and affordability to food may be at interplay for such high level of insufficiencies in rural households. It may also be noted that Agricultural and fishery laborer as well as extraction workers also display higher levels of caloric deprivation (39% households). In urban areas, caloric deprivations are highly concentrated among households engaged in elementary occupation related to sales and services (27% households) whereas legislators, professionals and managers have very low estimated prevalence of caloric deprivation.
Among rural households, multilevel linear regression estimates (Table 3) indicate that compared to agricultural and fishery laborers, households of legislators and senior officials, life science and health professionals, and market oriented skilled agricultural and fishery workers have significantly higher average per capita calorie intake. While a number of other service sector professionals depict higher household calorie intake, but the differences are not statistically significant. Among urban households, a large number of households from service sector background as well as those engaged in market oriented skilled agricultural and fishery work report significantly higher levels of calorie intake. There is no significant difference in calorie intake of low-end occupations and laborers in urban or rural areas. Further in Table 3, we also present the multilevel logistic regression estimates for the association of caloric intake and occupational background while adjusting for demographic and socioeconomic factors such as age and sex of household health, household size, and education of household head, religion, social group, and household wealth quintile. The estimates suggest that in rural areas, only households engaged in market oriented skilled agricultural and fishery works have significantly (at 5% level) lower odds ratio (OR: 0.72, with 95% CI: 0.63; 0.82) of having insufficient calorie intake compared to the agricultural and fishery laborer households. While households with professionals and managers also depict lower odds but the effects are significant only at 10% level. In urban areas, a similar relative advantage is discernible for market orientation and skills among agricultural and fishery worker households (OR: 0.72, with 95% CI: 0.59; 0.86). The odds of receiving insufficient calorie intake are also much lower for the service sector professionals, particularly life science and health professionals (OR: 0.33, with 95% CI: 0.13; 0.81). However, households engaged in sales and services based elementary occupations (OR: 1.35, with 95% CI: 1.04; 1.75) are 35% more likely to have insufficient caloric intake compared to the agricultural and fishery laborer households. Interestingly, those engaged in sales and services elementary occupations in urban areas have much higher odds of having insufficient caloric intake. This is perhaps due to the factors pertaining to their urban working lifestyle and age-specific behavioral aspects.
To account for the variations in the age and sex composition of the household members, table S2 present regression estimates taking calorie intake per consumer unit as outcome variable. The estimates pattern was consistent across these models as well. For example, compared to agricultural, fishery and related labourers the average calorie intake per consumer unit per household was significantly higher among those workers with market orientation skills in both rural as well as urban areas (Table S2). Logistic models also suggest that households engaged in market oriented agricultural and fishery works have significantly lower odds of caloric deprivation per consumer unit both in rural (OR: 0.80; 95% CI: 0.68; 0.94) as well as urban (OR: 0.78; 95% CI: 0.62; 0.99) areas. Even after adjusting for households’ landholding, the estimates were consistent, However, the estimates suggest that higher landholding of household is significantly associated with lesser probability of caloric deprivation. Among socioeconomic correlates, households’ wealth status, and education of household head was observed to be positively associated with caloric intake. Households with highly educated and female head were observed to have significantly higher calorie intake.
For rural and urban India, Table 4 presents the variance partition coefficients (VPC) for the multilevel linear regression model for average per capita calorie intake and multilevel logistic regression model for households having insufficient caloric intake. The models use five levels wherein the nesting runs in a hierarchical manner starting from households, occupational groups, districts, region and state of residence. The VPC can reveal the between-group variations attributable at the various levels. In this regard, the null model for average per capita calorie consumption in rural India shows that 10.2% of the total variance in this indicator is attributable to differences in occupational groups whereas state-related differences account for 7.4% of the variation in calorie intake. After adjusting of demographic and socioeconomic factors, the VPC of occupational groups declines to 7.8%. However, in urban areas, a greater proportion of variability in calorie intake is attributable to occupational group related differences (VPC 18.1% null model: VPC 14.8% fully adjusted model). The geographic boundaries of states and districts have low relevance in explaining variability across urban areas. In fact, region of residence has very low relevance in explaining variations in either outcome across rural or urban India. Further, the VPCs from logistic regression for households having insufficient caloric intake also present similar insights.
Finally, the occupational group-specific random intercepts from the four respective null models are plotted in Figure 2. It is inferred that in rural India caloric intake of about two-thirds of the occupational groups cannot be distinguished from the overall average (Figure 2A). However, more significant differences are apparent in urban areas (Figure 2B). In particular, most of the legislators, professionals and managers have a higher average intake. Figure 2C and 2D reveal that households engaged in mining, construction, manufacturing and transport labor activities are at an elevated risk of insufficient caloric intake.
It is worth noting the limitations of the analysis that can be largely associated with the nature of survey and the data. First, given the cross-sectional design, the results do not necessarily reveal the casual direction of association between occupation and caloric intake even though this does not impact the results regarding occupation-specific disparities and advantages in caloric intake. Second, although the NCO 2004 classification is sufficiently disaggregated to arrive at some meaningful inferences, but further disaggregation is advisable to understand the intricacies associated with skilled occupations within agriculture and fishery sectors. In fact, in the survey the NCO 2004 codes are missing for about 7.3% households and this can have a certain influence on the relative significance of the estimates. Third, the outcome indicator of household calorie consumption does not provide adequate insights regarding individual-level differences. Besides, to some extent, this indicator marginally underestimates the total calorie intake because of non-inclusion of food consumed outside the home . Finally, the regression analysis did not account for seasonal variations in availability and prices of food items which may consequently affect the calorie consumption. However, keeping the mind the research objectives, this limitation may not affect much the inferences made in the study.