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This paper aims to understand housing demand of urban Indian households in terms of housing and household-level characteristics. Because a house is a bundle of certain characteristics which vary across houses, each characteristic has an implicit price. Finding this implicit price for certain important characteristics is the first objective of this study. The second objective of the paper is to compute the income elasticity and price elasticity of housing demand for these cities.

To achieve comparable estimates, household-level data from India’s National Sample Survey Organisation housing surveys for the years 2002 and 2008-2009 have been used. A hedonic price function is estimated using ordinary least squares (OLS) and Box-Cox functional forms to estimate the implicit prices of housing characteristics. This exercise is attempted for owned and rented houses separately. Demand function required for computing the elasticities, uses the hedonic price index derived from the implicit prices and household characteristics.

The study finds housing demand to be income elastic and price inelastic for the six cities across both the time periods.

Firstly, this study includes housing characteristics such as individual access to drinking water, modern sanitation facility, separate kitchen, condition of the structure, existence of a road with street light and whether the house is in a slum or non-slum area in the hedonic price function. These variables were not used in any of the earlier studies pertaining to India. Secondly, it uses the Box-Cox non-linear form to derive the hedonic price function, a specification not used earlier. Thirdly, this is the first study analysing housing demand across the six largest Indian cities.

This chapter is an up-to-date survey of the state-of-the art in consumer demand analysis. We review (and evaluate) advances in a number of related areas, in the spirit of the recent survey paper by Barnett and Serletis (2008). In doing so, we only deal with consumer choice in a static framework, ignoring a number of important issues, such as, the effects of demographic or other variables that affect demand, welfare comparisons across households (equivalence scales), and the many issues concerning aggregation across consumers.

This chapter presents an exposition of the Generalized Fechner–Thurstone (GFT) direct utility function, the system of demand functions derived from it, other systems of demand functions from which it can be derived, and its purpose and the econometric circumstances that motivated its original development. Its use in econometrics is demonstrated by an application to household consumer survey data which explores the relationship between prices, on the one hand, and expected exogenous preference changers such as household size, schooling of heads of household, and other social factors, on the other.

Extends Mäler′s notion of weak complementarity between a private good and a public good to non‐homothetic demand functions which can be exactly aggregated. Aggregate demand functions depending on private prices, public good quantities and income distribution statistics can then be used to recover the private individual demand functions which reveal an individual′s willingness to pay for public goods.

The COVID-19 pandemic has forced countries to consider how to reach vulnerable communities with extended outreach services to improve vaccination uptake. The authors created an optimization model to align with decision-makers' objective to maximize immunization coverage within constrained budgets and deploy resources considering empirical data and endogenous demand.

A mixed integer program (MIP) determines the location of outreach sites and the resource deployment across health centers and outreach sites. The authors validated the model and evaluated the approach in consultation with UNICEF using a case study from The Gambia.

Results in The Gambia showed that by opening new outreach sites and optimizing resource allocation and scheduling, the Ministry of Health could increase immunization coverage from 91.0 to 97.1% under the same budget. Case study solutions informed managerial insights to drive gains in vaccine coverage even without the application of sophisticated tools.

The research extended resource constrained LMIC vaccine distribution modeling literature in two ways: first, endogenous calculation of demand as a function of distance to health facility location enabled the effective design of the vaccine network around convenience to the community and second, the model's resource bundle concept more accurately and flexibly represented complex requirements and costs for specific resources, which facilitated buy-in from stakeholders responsible for managing health budgets. The paper also demonstrated how to leverage empirical research and spatial analysis of publicly available demographic and geographic data to effectively represent important contextual factors.

Artificial intelligence (AI) has become an input to the production of goods and services. Therefore, a general question is there that “How the labor hour/human resource will be replaced by the artificial intelligence?” To answer this question, the paper considers that both AI and the human resources (HR) are the inputs to the firm and explains the choice between the two with reference to the customer relationship management. The paper derives the individual firms and the industry demand functions of the AI and the HR when both are present in the production of the identical or closely related goods and services. Moreover, the paper also shows the strategic behavior of an individual firm with the industry in selecting the AI and the HR. It has been shown that the individual firm's choice in the industry depends on the choice of the industry leader. The paper explains the supermodular game between the firms in an industry.

Game theory, industrial organization and non-convexity theories have been used in this paper to identify the choice between the HR and the AI in the customer relationship management.

The paper explains analytically the preference and demand for AI in the industry. Individual firm's strategic behavior and decision on choosing AI and the industry equilibrium have been studied logically. Moreover, the paper gives some light on the question of employment in presence of AI. The paper proves that in the presence of AI, labor demand will not be reduced but both will be used.

This work proves for the first time using some logical derivation that AI will not crowd out labor from the market. Moreover, to run AI, labor should also be used. It has been proved that to complete a job with speed and quality, both AI and HR are to be used.