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This chapter tries to capture the disparity in expenditure on primary education based on gender among the religious groups (Hindu, Muslim, and Christian) in rural India. The gender gap in education expenditure for a certain demographic group is calculated using the Oaxaca–Blinder decomposition approach. Further, we tried to identify the various household-related factors which might influence the decision of spending on a child's education. We used the 75th-level National Sample Survey Office (NSSO) unit-level dataset of July 2017 to June 2018 (one academic year) to obtain data on education expenditure and other household factors which play a manifesting role in the gender gap in expenditure on education. Our finding suggests that the total differential (log mean boys education expenditure-log mean girls education expenditure) is positive among all religious groups signifying the gender bias in education expenditure. We also found that the magnitude of the “Unexplained Effect” component is higher compared to the “Explained Effect” component signifying that the treatment of characteristics by students differs by their sex at elementary education. Household size and if household members are employed on a casual basis, then their expenditure on education falls on the other hand income of the household, a household with computer availability and household member engaged in regular wage/salary earning plays a positive role in expenditure on primary education in rural India.

Waste management is a crucial aspect of sustainable development, but is it economically sustainable for marginalized informal firms? The study tries to answer this question by revisiting the Porter–Wagner dilemma about the association between environmental management (EM) and firm performance (FP). The study looks into the various liquid waste management practices (LWMPs) adopted by them and the overall impact of LWMPs on firms' economic performance.

The study uses the latest available cross-sectional data source on Indian informal firms by the National Sample Survey Office (NSSO), 73rd survey round 2015–16. First, ordered logistic regression was used to analyse the factors that impact a firm's adoption of a particular LWMP. Subsequently, to capture the heterogeneity among the firms based on productivity and size, a quantile regression (QR) was employed to analyse the impact of LWMPs on firm productivity. Additionally, the propensity score matching technique was used to address endogeneity concerns.

The authors find that bigger, urban-located and female-owned firms adopt cleaner LWMPs that positively impact their economic performance. Furthermore, the QR analysis observed that the most productive firms could extract higher returns from adopting cleaner LWMPs, indicating the relevance of the Porter–Wagner dilemma, i.e. environmental and economic sustainability are possibly symbiotic, thus having a feedback mechanism.

To the authors’ limited knowledge, this is the first study analysing the relationship between EM and FP among the informal sector firms, which are away from any regulations or obligations. Since sustainability is a two-way process, policies should be devised that incentivise sustainable business practices.

This paper examines whether farmers' knowledge of the minimum support prices (MSPs) affects farm-gate prices. MSP is the minimum guaranteed price for agricultural commodities announced by the Government of India for 24 commodities. Most farmers in India prefer to sell their produce at the farm-gate due to a small marketable surplus and hence do not directly benefit from MSP. The authors test the common argument in the political discourse that if farmers have knowledge of MSP, then they can bargain with traders during the farm-gate transaction and demand a better price close to MSP.

The authors use matching methods to examine the impact of knowledge of MSP on farm-gate prices.

Using nationally representative data, the authors show that there is no empirical evidence that the knowledge of MSP of the crops leads to higher bargaining power and better farm-gate prices.

Price information (MSP in this case) alone cannot improve the bargaining power of farmers and result in a better price realization. As a safety net, MSP fails in the absence of procurement of products by the government. This also raises the question of the equitability of the price support system in India and calls for a rethink of the MSP policy.

This study is the first of its kind to examine the anchoring effect of knowledge of MSP on farm-gate prices using a nationally representative dataset.

This chapter deals with an important but neglected aspect of female labor force participation (FLFP) in urban India. Contemporary literature typically focuses on the entire urban sector and ignores one important aspect of urban living – the slums and its dwellers. This study fills that critical gap by examining two different household surveys side-by-side: a primary survey of households living in slums and slum-rehabilitated colonies, and the nationally representative Indian Human Development survey-II. This study brings outs a comparative picture of nature/type of FLFP and its various correlates from both slum and non-slum areas of three metro cities of India, viz. Delhi, Kolkata and Mumbai. It further explores the similarities and the differences of the correlates for FLFP among the slum clusters of these cities. It is found that despite being poorer and marginalized, the slum dwelling women’s LFP rate is not extra-ordinarily high vis-á-vis their non-slum urban counterparts. In slums, a higher proportion of women are engaged in self-employment (including family business) and casual employments (includes domestic helps), whereas in non-slum areas relatively more women are engaged in regular salaried jobs. Regression analysis identifies correlates that have similar effects, but with different intensity, across-the-board – relationship between education and FLFP reflects a flat-bottom J-shaped pattern; being married, higher child dependency ratio and household heads with higher education significantly constrain women’s work choice; strong income effect of other household members earning on FLFP, but asset holding has no bearing. However, there are other factors that affect FLFP differently in slums and non-slum areas. Policy prescriptions are drawn.

Non-farm employment has transitioned from a residual to a dominant livelihood option in rural India. Despite the sector’s diverse welfare implications, it is still a male-dominant sector with limited scope for female’s participation. Several socio-economic and cultural factors are responsible for such disparities in occupational choices. The purpose of this study is to examine this gender dimension of occupational diversification for rural India and focuses on the role of education, caste and land ownership in explaining employment probabilities across gender.

This study uses secondary data on employment and unemployment from the National Sample Survey Office (NSSO) for rural India and pooled the data for three periods that include 61st (2004–2005), 66th (2009–2010) and 68th (2011–2012) round comprising a total of 235,722 individuals. The study applies a multinomial logit regression model.

The results show that education facilitates females to diversify to sectors like manufacturing, mining and construction while educated males are more likely to diversify to services. However, the likelihood of diversification by educated females is low for those belonging to land-owning households. On the contrary, land ownership facilitates educated males to join sectors like mining and quarrying and services. It is also found that females belonging to Scheduled Tribe/Scheduled Caste (ST/SC) households diversify to low return activities like manufacturing and construction while males are more likely to join services.

This study has contributed to the literature on employment diversification by considering not only the gender aspect of diversification but also examining how education, caste and land would explain occupational choices between males and females. It is evident from the findings that education can be a liberating factor for females to participate actively in sectors outside agriculture but the status quo associated with land ownership in rural India declines their possibility of economic participation compared to males. Even educated females are confined to manufacturing and construction in the absence of proper non-farm employment opportunities for them.

Although they are neighbouring Asian countries with many similarities, India and Indonesia have different levels of household expenditure inequality. During the end of 2000s, the Gini coefficient of Indonesia was 9.1 percentage points larger than the Gini coefficient of India. To understand the determinants of this difference, this study decomposes it into the contribution of price effects, demographic effects and labour market structure effects. Differences in expenditure structures (price effects) and demographic characteristics are found to be the greatest contributors to the inequality gap across the two countries. The difference in the education distribution of household heads also has a positive and significant impact on the inequality gap. Differences in the labour market structure, on the other hand, turn out to be less important.

The present study examines the fluctuations in Socioeconomic and demographic (SED) factors and the prevalence of Non-Communicable Diseases (NCDs) across clusters of states in India. Further, it attempts to analyze the extent to which the SED determinants can serve as predictive indicators for the prevalence of NCDs.

The study uses three rounds of unit-level National Sample Survey self-reported morbidity data for the analysis. A machine learning model was constructed to predict the prevalence of NCDs based on SED characteristics. In addition, probit regression was adopted to identify the relevant SED variables across the cluster of states that significantly impact disease prevalence.

Overall, the study finds that the disease prevalence can be reasonably predicted with a given set of SED characteristics. Also, it highlights age as the most important factor across a cluster of states in understanding the distribution of disease prevalence, followed by income, education, and marital status. Understanding these variations is essential for policymakers and public health officials to develop targeted strategies that address each state’s unique challenges and opportunities.

The study complements the existing literature on the interplay of SEDs with the prevalence of NCDs across diverse state-level dynamics. Its predictive analysis of NCD distribution through SED factors adds valuable depth to our understanding, making a notable contribution to the field.