Rural Indian Households Research Articles

Flickering lifelines: Electrification and household welfare in India

This study moves beyond counting electrified households as the policy consensus on electrification and examines the effect of reliability of electricity on household's welfare in India. We analyse two household surveys covering the period from 2005 to 2018, and examine the effect of additional hours of electricity using panel fixed effects instrumental variables regressions. We focus on the intensive margins of deficiency, i.e. how additional hours of electricity in a day affects household's consumption expenditure, income, amenities, assets, borrowing and the status of poverty among others. Results show significant effects of an additional hour of electricity overall, especially among the poor households in rural India. The findings are robust to alternative ways of measuring household income, the use of alternative datasets to measure the effects on reliable electrification, as well as other robustness checks. The study recommends progressive pricing with targeted subsidies for the electrified households to increase household welfare while reducing the financial losses of the State Electricity Boards.

Treatment of Dairy Industry Wastewater - Special Reference to Design of Aerated Lagoon

Industrial revolution brought diversified industries to produce goods to meet the demand of increasing population. Such a rapid growth of industrial sector caused water pollution to a great extent. Dairy industry is not an exception, causing significant water pollution. The dairy sector in India grew at a rate of 6.4 per cent annually in the last four years against the global growth rate 1.7 per cent demonstrating significant increase in milk productivity. Around 80 million rural Indian households are engaged in milk production with very high proportion being landless, small and marginal farmers. But on the other hand, water pollution being caused from these dairy industries is quite significant. Various biological treatment technologies were tried for treating dairy wastewater. An attempt has been made by the authors of the present paper to design aerated lagoon including screen chamber, primary clarifier, quiescent settling zone and sludge disposal with success to treat dairy wastewater.

Social identity as a driver of adult chronic energy deficiency: analysis of rural Indian households.

This study investigates the effect of social identity (caste, religion, gender, and asset) on adult chronic energy deficiency (CED) using the Indian Human Development Survey (IHDS) data from 2005 and 2011. From 2005 to 2011, out of 63,323 adult individuals, four-fifths (85.7%) of men and two-fifths (44.7%) of women improved their health status from CED to non-CED. The lowest improvement was observed among Dalit (Scheduled Casts, Scheduled Tribes) women: 41.9% moved from CED to non-CED status. We also find significant differences in prevalence of CED between Dalit and non-Dalit individuals and households. To achieve a significant improvement in CED, programs targeting malnourishment must be prioritized the Dalit households.

Proof of Concept of a Gamified DEvelopmental Assessment on an E-Platform (DEEP) Tool to Measure Cognitive Development in Rural Indian Preschool Children.

Over 250 million children in developing countries are at risk of not achieving their developmental potential, and unlikely to receive timely interventions because existing developmental assessments that help identify children who are faltering are prohibitive for use in low resource contexts. To bridge this “detection gap,” we developed a tablet-based, gamified cognitive assessment tool named DEvelopmental assessment on an E-Platform (DEEP), which is feasible for delivery by non-specialists in rural Indian households and acceptable to all end-users. Here we provide proof-of-concept of using a supervised machine learning (ML) approach benchmarked to the Bayley’s Scale of Infant and Toddler Development, 3rd Edition (BSID-III) cognitive scale, to predict a child’s cognitive development using metrics derived from gameplay on DEEP. Two-hundred children aged 34–40 months recruited from rural Haryana, India were concurrently assessed using DEEP and BSID-III. Seventy percent of the sample was used for training the ML algorithms using a 10-fold cross validation approach and ensemble modeling, while 30% was assigned to the “test” dataset to evaluate the algorithm’s accuracy on novel data. Of the 522 features that computationally described children’s performance on DEEP, 31 features which together represented all nine games of DEEP were selected in the final model. The predicted DEEP scores were in good agreement (ICC [2,1] > 0.6) and positively correlated (Pearson’s r = 0.67) with BSID-cognitive scores, and model performance metrics were highly comparable between the training and test datasets. Importantly, the mean absolute prediction error was less than three points (<10% error) on a possible range of 31 points on the BSID-cognitive scale in both the training and test datasets. Leveraging the power of ML which allows iterative improvements as more diverse data become available for training, DEEP, pending further validation, holds promise to serve as an acceptable and feasible cognitive assessment tool to bridge the detection gap and support optimum child development.

Market Access, Production Diversity, and Diet Diversity: Evidence From India.

Recent literature, largely from Africa, shows mixed effects of own-production on diet diversity. However, the role of own-production, relative to markets, in influencing food consumption becomes more pronounced as market integration increases. This paper investigates the relative importance of two factors - production diversity and household market integration - for the intake of a nutritious diet by women and households in rural India. Data analysis is based on primary data from an extensive agriculture-nutrition survey of 3600 Indian households that was collected in 2017. Dietary diversity scores are constructed for women and households is based on 24-hour and 7-day recall periods. Household market integration is measured as monthly household expenditure on key non-staple food groups. We measure production diversity in two ways - field-level and on-farm production diversity - in order to account for the cereal centric rice-wheat cropping system found in our study locations. The analysis is based on Ordinary Least Squares regressions where we control for a variety of village, household, and individual level covariates that affect food consumption, and village fixed effects. Robustness checks are done by way of using a Poisson regression specifications and 7-day recall period. Conventional measures of field-level production diversity, like the number of crops or food groups grown, have no significant association with diet diversity. In contrast, it is on-farm production diversity (the field-level cultivation of pulses and on-farm livestock management, and kitchen gardens in the longer run) that is significantly associated with improved dietary diversity scores, thus suggesting the importance of non-staples in improving both individual and household dietary diversity. Furthermore, market purchases of non-staples like pulses and dairy products are associated with a significantly higher dietary diversity. Other significant determinants of dietary diversity include women's literacy and awareness of nutrition. These results mostly remain robust to changes in the recall period of the diet diversity measure and the nature of the empirical specification. This study contributes to the scarce empirical evidence related to diets in India. Additionally, our results indicate some key intervention areas - promoting livestock rearing, strengthening households' market integration (for purchase of non-staples) and increasing women's awareness about nutrition. These are more impactful than raising production diversity.

What determines women's agricultural participation? A comparative study of landholding households in rural India

Abstract This paper examines the key socioeconomic and cultural-demographic factors that determine rural women's labour contributions in agriculture in India, both on family farms (either as cultivators or as family labour) and as agricultural wage labourer. Based on the analysis of primary data derived from a survey of 800 households from the two Indian states of Gujarat and West Bengal, it establishes that women's work in the farm sector cannot be homogenized. Women's work as additional hands in family farms differs from that as wage labourers which is casual in nature; their work also differs across different regions. In the commercialized, relatively more developed state of Gujarat, women's labour contributions are significantly different from West Bengal's less commercialized agrarian economy. The paper concludes that feminization of agriculture in India is distress-led where it has both class (defined with income in Gujarat) and caste (social groups) connotations in Gujarat, while mainly economic factors influence women's work in the farm sector in West Bengal.

What’s Your Child Worth? An Analysis of Expected Dowry Payments in Rural India

Abstract This paper introduces a new primary dataset on dowry payments in rural India. A novel feature of the data is that in addition to eliciting actual dowry payments, we also asked rural Indian households how much dowry they expected to pay/receive for each of their currently unmarried children. These expectations are presumably the basis of household decision making (as opposed to actual dowries paid and received) with respect to decisions such as human capital investments, saving and consumption, etc. We find that the distribution of dowry forecasts appears to be consistent with the distribution of actual dowry payments, suggesting that dowry perceptions may be quite accurate. Our analysis of dowry forecasts further indicates that (i) individual-specific attributes are significantly more important than household income and wealth in terms of explaining the magnitude of dowry payments, and (ii) bride quality is also a significant determinant of dowry, which provides a corrective to the emphasis on groom quality in the literature.

Weathering electricity demand? Seasonal variation in electricity consumption among off-grid households in rural India

We investigate the relationship between energy consumption from standalone PV systems and a factor widely understood as critical to general energy use: weather. Using household level data collected between January 2016 and January 2017 from seven habitations in Uttar Pradesh, India, we employ linear regression models with quadratic time polynomials to estimate the influence of several key weather variables on electricity consumption. The results highlight the substantive influence of seasonal variation on energy demand. With each single degree increase in temperature, electricity consumption increased 5.2-Wh per week on average when controlling for seasonal change. This strong, positive relationship between electricity consumption and temperature indicates that weather is an important consideration for electrification efforts. As current projections indicate average temperatures will continue to rise, off grid system design strategies should consider the potential for electricity consumption to rise as well.

Economic Burden of COVID-19 Lockdown on the Poor

The COVID-19 pandemic caused the largest number of lockdowns across the globe, affecting nearly half of the world’s population by the first week of April 2020. The biggest lockdown was the one declared by the Indian government. All economic activity was shut down overnight, impacting the lives of the nation’s 1.3 billion people. The economic impacts of lockdowns on the poor are little understood because the data have not been available. We have been tracking the weekly financial activities of a sample of poor households in rural India for over a year. Using this unique high-frequency weekly data before India’s COVID-19 lockdown and supplementing that dataset with phone-call surveys conducted on the same households for four weeks after the lockdown announcement, we econometrically estimate the impacts of the lockdown. Our analysis reveals that in the first four weeks after India’s lockdown announcement, poor households lost INR 1,022 (US$ 13.5), which is about 88 percent of their average weekly income from the previous year. In addition to the income loss, households reduced meal portions and consumed fewer food items. The poorer households were hit harder, but government food aid slightly mitigated the negative impacts.

Seasonal Migration and Feminization of Farm Management: Evidence from India

Using gender-disaggregated data on land operations from India, this paper contributes to the literature on agricultural feminization by showing a relationship between seasonal or short-term migration for work and feminization of farm management. Using a nationally representative data-set covering 35,604 rural Indian households in 2013, we identify if women are taking on the role of farm managers in households with short-term migrants. Our results show that women are less likely than men to be decision makers on farms, but this dynamic changes when there is short-term migration in the household; the probability of women being decision makers on farms increases. These results are robust to concerns over omitted variables, endogeneity and sample selection issues. Our study highlights the importance of unpacking the feminization process to better understand the role of women as farm managers and the need for supporting this transition to ensure women farmers realise their full potential.

Here Comes the Rain Again: Productivity Shocks, Educational Investments and Child Work

In predominantly agrarian economies with limited irrigation, rainfall plays a critical role in shaping households' incomes and subsequently their spending decisions. This study uses household-level panel data from a nationally representative survey in India to estimate the effect of agricultural productivity shocks – as proxied by exogenous annual rainfall deviations from long-term average – on education expenditures and children's work status in rural Indian households. Our results show that a transitory increase in rainfall significantly reduces education expenditures and increases the likelihood of child labor across a range of work activities. Additionally, we show that productivity-enhancing inputs such as land ownership and credit access do not mitigate these countercyclical effects of rainfall variations, indicating the importance of market imperfections (in labor and land markets). We also find that the effects of productivity shocks are reinforced for historically marginalized castes, and moderated for more educated households. These highlight that the average effects mask considerable heterogeneity based on household and regional characteristics.

Enhancing food security through diet quality: The role of nonfarm work in rural India

AbstractIndia has achieved food security at the macro level. However, at the micro level, the country still struggles with extensive problems of food nutrition insecurity. In this paper, we assess the impact of nonfarm income and nonfarm work status (casual and full‐time nonfarm work) of operator, spouses, and couples on the diet quality of smallholder households in India. We find that nonfarm income decreases the likelihood of farming household being in the poor‐diet quality group by 31% and the medium‐diet quality group by 3%. Full‐time nonfarm work by operators and spouses decreases the likelihood of farming households being in the poor‐diet quality group by 3% and 9%, respectively. Finally, national programs like public food distribution programs increase the probability of rural farming households in the poor‐diet quality group. Findings from this study underscore the importance of nonfarm income and full‐time nonfarm work in improving diet quality of rural Indian households.

Exploring the Potential of a Gamified DEvelopmental Assessment on an E-Platform (DEEP) Tool to Measure Cognitive Development in Rural Indian Preschool Children

Background: Over 250 million children in developing countries are at risk of not achieving their developmental potential, and unlikely to receive timely interventions because existing developmental assessments that help identify children who are faltering are prohibitive for use in low resource contexts. To bridge this ‘detection gap’, we developed a tablet-based, gamified cognitive assessment tool named DEvelopmental assessment on an E-Platform (DEEP), which is feasible for delivery by non-specialists in rural Indian households and acceptable to all end-users. Methods: Here we provide proof-of-concept of using a supervised machine learning (ML) approach benchmarked to the Bayley’s Scale of Infant and Toddler Development, 3rd Edition (BSID-III) cognitive scale, to predict a child’s cognitive development using metrics derived from gameplay on DEEP. Two-hundred children aged 34-40 months recruited from rural Haryana, India were concurrently assessed using DEEP and BSID-III. 70% of the sample was used for training the ML algorithms using a 10-fold cross validation approach and ensemble modelling, while 30% was assigned to the ‘test’ dataset to evaluate the algorithm’s accuracy on novel data. Findings: Of the 522 features that computationally described children’s performance on DEEP, 31 features which together represented all nine games of DEEP were selected in the final model. The predicted DEEP scores were in good agreement (ICC [2,1] > 0.6) and positively correlated (Pearson’s r = 0.67) with BSID-cognitive scores, and model performance metrics were highly comparable between the training and test datasets. Importantly, the mean absolute prediction error was less than three points (<10% error) on a possible range of 31 points on the BSID-cognitive scale in both the training and test datasets. Interpretation: Leveraging the power of ML which allows iterative improvements as more diverse data become available for training, DEEP holds promise to serve as an acceptable, feasible and validated cognitive assessment tool to bridge the detection gap and support optimum child development. Funding Statement: This work was funded by the Corporate Social Responsibility (CSR) initiative of Madura Microfinance Ltd. Declaration of Interests: Dr Tara Thiagarajan, a collaborator from Sapien Labs in her scientific capacity, also holds the position of Chairperson of Madura Microfinance Ltd. The other authors declare no competing interests. Ethics Approval Statement: This study was conducted in accordance with the Declaration of Helsinki and approved by the institutional ethics committees of the Public Health Foundation of India and Sangath. Prior to data collection, the objectives and methods of our study were explained to the parent and written informed consent was obtained from those who agreed to participate in this study.

Measuring open defecation in India using survey questions: evidence from a randomised survey experiment

ObjectivesTo investigate differences in reported open defecation between a question about latrine use or open defecation for every household member and a household-level question.SettingRural India is home to most of...

Does Non-farm Income Affect Food Security? Evidence from India

Livelihood diversification through greater non-farm activities has been considered as an important mechanism to propel growth, lower rural poverty and augment farm income across developing countries. Little, however, is known about its implications for nutritional outcomes such as dietary diversity. Using a nationally representative panel survey of rural households in India, and night-time light intensity as an instrumental variable (IV) for non-farm income, we show that engaging in non-agricultural livelihood has a positive effect on overall food expenditure, especially on non-cereal items, enabling greater dietary diversity. These findings have crucial policy implications for nutrition transition in India where agricultural incomes have been stagnant during the last decade. Our findings further contribute to the existing knowledge of agriculture-nutrition pathways.

Cardiovascular risk factor reduction by community health workers in rural India: A cluster randomized trial

There is a need to identify and test low-cost approaches for cardiovascular disease (CVD) risk reduction that can enable health systems to achieve such a strategy. Community health workers (CHWs) are an integral part of health-care delivery system in lower income countries. Our aim was to assess impact of CHW based interventions in reducing CVD risk factors in rural households in India. We performed an open-label cluster-randomized trial in 28 villages in 3 states of India with the household as a unit of randomization. Households with individuals at intermediate to high CVD risk were randomized to intervention and control groups. In the intervention group, trained CHWs delivered risk-reduction advice and monitored risk factors during 6 household visits over 12 months. Households in the non-intervention group received usual care. Primary outcomes were a reduction in systolic BP (SBP) and adherence to prescribed BP lowering drugs. We randomized 2312 households (3261 participants at intermediate or high risk) to intervention (1172 households) and control (1140 households). At baseline prevalence of tobacco use (48.5%) and hypertension (34.7%) were high. At 12 months, there was significant decline in SBP (mmHg) from baseline in both groups- controls 130.3 ± 21 to 128.3 ± 15; intervention 130.3 ± 21 to 127.6 ± 15 (P < .01 for before and after comparison) but there was no difference between the 2 groups at 12 months (P = .18). Adherence to antihypertensive drugs was greater in intervention vs control households (74.9% vs 61.4%, P = .001). A 12-month CHW-led intervention at household level improved adherence to prescribed drugs, but did not impact SBP. To be more impactful, a more comprehensive solution that addresses escalation and access to useful therapies is needed.

How do droughts impact household food consumption and nutritional intake? A study of rural India

Abstract This paper investigates the impacts of droughts on food expenditure and macronutrient consumption among rural Indian households. To isolate causal effects, I exploit random year-to-year variation in a dry shock, defined as the absolute deviation of rainfall below its long-run mean. I find that the dry shock has a statistically significant and negative effect on household nutrition. For a median dry shock, I estimate that households spend 1 percent less per capita per month on food and consume up to 1.4 percent fewer calories, protein, and fat. Disaggregating the effects by food group, I demonstrate that household diets become less balanced as a result of droughts: the dry shock leads households to rely primarily on cereals and to purchase less vegetables, fruits, pulses, and animal-sourced foods. Hence, droughts negatively impact not only the quantity but also the quality of rural household diets. Finally, I explore the potential channels for these effects. I argue that rather than higher food prices, a decline in household market and non-market income is the primary reason for lower household food consumption and nutrition during droughts. Taken together, these findings suggest that attaining food security amid extreme weather conditions requires an integrated approach that focuses on food not only for survival but also for leading a healthy and active life.

Correlates of agrarian indebtedness in rural India

Purpose The purpose of this paper is to understand the factors determining the incidence and extent of indebtedness among agricultural households in rural India. Design/methodology/approach This study is based on a nationally representative survey carried out under the 70th Round of the National Sampling Survey Office (NSSO) across rural India. Data on household characteristics, farming characteristics, indebtedness and extent of outstanding credit have been extracted from the comprehensive survey data. Four research hypotheses have been formulated and tested using simple statistical techniques. Further, using the Heckman Selection Model, the study assesses the factors determining the agrarian indebtedness among households in rural India. Findings The results from the descriptive analysis show that there is a significant difference in socio-economic and farm characteristics of indebted and non-indebted households. Further, the level of indebtedness differs across sources of the loan, landholding sizes and geographical locations among agricultural households. The results of regression analysis clearly indicate that household characteristics, farm characteristics and sources of loan determine both the incidence and extent of indebtedness among agricultural households. Research limitations/implications The main limitation of the study is that only the data giving information regarding the amount of outstanding loans have been collected, and there is no information regarding the amount of credit availed, the purpose and the due date of payment. Further, there is scope to improve the robustness of the empirical model by adding and modifying explanatory variables. Originality/value There are only a limited number of empirical studies providing an understanding of the factors determining the indebtedness of agricultural households in rural India. Hence, this study is a good value addition to the existing literature.

A sequential, exploratory, mixed-methods approach for development and validation of a context-specific knowledge, attitude and practice questionnaire on micronutrients for literate mothers of school-age children.

To develop and validate a context-specific comprehensive knowledge, attitude and practice (KAP) questionnaire for literate mothers on factors affecting micronutrient status of 6-10-year-old children. Sequential exploratory mixed-methods study using focus group discussions (FGD) and psychometric validation. Seven randomly selected villages with &gt;500 households with three to five family members each, in Ghatkesar sub-district, Medchal district, Telangana state, India.ParticipantsLiterate mothers from middle-income households with at least one child (6-10 years) for FGD (n 44), for testing the internal consistency (n 80) and for test-retest reliability (n 30). The themes for FGD were diet diversity, micronutrients, cooking and eating practices, national programmes and sunlight exposure. Knowledge among caregivers about sources of micronutrients, deficiency symptoms, cooking/eating practices was low, while attitude towards diet diversity and sunlight was good. Non-availability of fruits and vegetables was a barrier to diet diversity. About 72 % of the questions from the item pool were based on FGD. After content validity, 125 items were selected for the questionnaire which, upon psychometric validation, was reduced to an eighty-eight-item questionnaire with difficulty index of 0·10-0·91, discrimination index of 0·09-0·68, Cronbach's α of 0·78 (reliability of knowledge and attitude) and 0·50 (practice). A Bland-Altman plot showed good agreement between test and retest scores. The questionnaire developed and validated using a sequential exploratory mixed-methods approach can be used for assessing KAP on micronutrients and factors affecting consumption of diverse diets in rural Indian households.

Dietary Transition in India: Temporal and Regional Trends, 1993 to 2012.

Rapid economic growth, urbanization, and globalization have resulted in dietary transformation in India. Triple burden of malnutrition remains a significant concern, with high prevalence of undernutrition, widespread micronutrient deficiencies, and rising obesity. This article reviews the dietary transition in India by analyzing trends in food consumption across time and space. Household consumption survey data from 1993 to 2012 are analyzed to examine both national- and state-level trends to investigate how diets have changed and vary across the country. Typical Indian diets are characterized using k-mean cluster analysis and associated with socioeconomic and geographical aspects. The article finds that on average Indian household diets have diversified slowly but steadily since the 90s. Indians diets have shifted away from cereals to higher consumption of milk. However, progress on micronutrient-rich food groups such as fruits, vegetables, meat, and egg has been worryingly slow. Even by 2012, about a fifth of rural Indian households did not consume fruits or milk, while more than half of both urban and rural households did not consume any meat, fish, or eggs. Five predominant dietary types are identified. Sections of the Indian households do consume reasonably balanced diets, but large percentages consume cereal-focused, dairy-focused, or processed food heavy diets with high processed food content. Diets in India have not transformed sufficiently to overcome major gaps in intakes of micronutrient-rich foods. Large regional heterogeneities in diets call for regionally differentiated strategies to improve diets.