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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.

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.

The purpose of this paper is to determine the inventory replenishment policies when demand rate is a function of the inventory space allocated to the products on retail shelves. Existing results on inventory policies with inventory-level-dependent demand (ILDD) assume deterministic functional forms of the demand rate. In this paper, the authors model the inventory decisions when demand is a function of shelf-space allocation and random uncertainty. The authors provide managerial insights of this paper's results.

The demand rate is assumed to be a function of shelf-space allocation based on two settings in the literature. First, the authors model the demand rate as a function of initial shelf-space allocation. In the next setting, the authors assume that the demand rate is a function of instantaneous inventory level on shelves. In both the settings, the authors also model random demand uncertainty in addition to the shelf-space dependency of demand rate. The objective is to maximize the expected profit and determine the inventory parameters.

In addition to the demand uncertainty, the authors consider linear, power and exponential functional forms of demand rate. Inventory policy that maximizes expected profit is determined when demand rate is a function of initial allocation and displayed inventory level. The results are implementable for practitioners for optimizing the shelf-space allocation and related inventory policy.

Most of the extant results on inventory policy with shelf-space-dependent demand do not model the demand uncertainty. The authors model a variety of functional forms of demand rate with ILDD in addition to the demand uncertainty. The results are a building block for more applications in inventory management for real-life applications.

Aims to analyse the labour market outcome when there are two unions in the industry, representing heterogeneous workers – imperfect substitutes in production.

Competition between union policies are viewed in terms of both employment and wage strategies. Results for substitutes and complements are inspected. Attention is given to the strategic behaviour of the unions, towards one another and/or the employer side. Cooperation is modelled using the Nash‐maximand approach.

Gathers some notes and enlargements to the standard collective bargaining problem in which unions maximise utility. Extends the framework to model union competition behaviour for jobs and/or employment that reproduces the standard market product analysis of imperfect competition. Focuses on heterogeneous labour.

The analysis concentrates on the case of union duopoly, but can easily be enlarged to the n‐union setting – which is left for further investigation.

A simple analytical example with Stone‐Geary union utility functions and a linear labour demand system is forwarded.

Marx’s monetary theory is an important part of Marxist economics and an irreplaceable milestone in the intellectual history of the monetary theory. The purpose of this paper is to summarize the main content of Marx’s monetary theory from three aspects: the source and nature of money, the function of money and the historical significance of money.

Moreover, this paper also gives an extended understanding of Marx’s monetary theory from four perspectives: the endogenous credit mechanism of money, the functions of money and demands for money, the financial function of money and the economic and social functions of money.

Lastly, the present paper discusses the practical significance of Marx’s monetary theory from three perspectives, namely, the inspection of “Bitcoin” from the nature and function of money, the definition of demands and the division of supplies at the monetary level, and the prevention of systemic financial risks and the focus of financial supervision.

Marx’s monetary theory is an important part of Marxist economics and an irreplaceable milestone in the intellectual history of the monetary theory. However, for a long time, the contribution of Marx has rarely been mentioned in the intellectual history of monetary theory. Even the book, Political Economy (On Capitalism), has been only summarily concerned with the source and function of money in Marx’s monetary theory, rather than revealing Marx’s outstanding contribution in the monetary theory and the financial connotation of Marx’s monetary theory, and expounding its practical significance.

The purpose of this paper is to utilize a cost and revenue driver model for commercial banking to examine the differential effects of the drivers within and between banking functions, and to examine the role of information technology (IT) in moderating the relationship between costs and cost drivers and revenue and revenue drivers.

The model is estimated on a cross‐sectional sample of 121 banks from the functional cost and profit analysis data set collected by the Federal Reserve Banks. Multivariate regression analysis with interaction terms is utilized to examine the differential impact of IT in two contrasting banking functions.

The results document the role of transactional IT on the cost driver relationships in the labor cost models in both the demand deposit and commercial loan functions. The role of strategic IT in the revenue driver models is documented for the demand deposit function but not for the commercial loan function.

Only two banking functions are selected. Expanding the model and testing it on other banking functions may be useful.

By disaggregating the IT variable and incorporating IT in a cost and revenue driver model managers can utilize the model to examine the impact of IT in banking.

A model that disaggregates the IT variable by allocating support costs to functions and delineates links between IT variables and cost and revenue drivers in banking.

This study examines the potential of contracts as one of the supply chain coordination mechanisms under competitive conditions. It also investigates a two-echelon supply chain model with two manufacturers and two retailers to develop a competitive structure in grey stochastic demand.

Supply chain demand is considered as a stochastic phenomenon depending on the selling price of the product. Also, products can be replaced by market manufacturers. Each retailer faces the pricing of products from two manufacturers, leading to competition between downstream retailers. In the present study, the duopoly supply chain model was presented based on the wholesale price contract, revenue-sharing contract and quantity discount contract separately.

Grey optimization and analysis of their coordination were presented. The results showed the high performance of revenue-sharing contracts in the supply chain. Thus, manufacturers will give the next priority to quantity discount contracts.

Ordering is the main factor contributing to competitive decision-making. Meanwhile, decision-making along with ordering and pricing will be required due to the nature of the demand.

The study develops a mathematical model of the firm to derive theoretical foundations for the balanced scorecard concept (BSC). The model is based on several parts which are integrated into a company model. This model includes the demand function, the production function and the objective function of the firm which are depicted by traditional microeconomic concepts. Demand is presented as a function of price and customer relationship management (CRM) costs. Production is assumed to depend on labor, capital, and development and learning (D&L) costs. Simple dynamics is included both in the demand and production function. The strategy of the firm is depicted by the objective function based on profit and net sales. The output variables of the model are classified as the four perspectives of BSC. The effects of the objectives (strategies) on the importance (shadow prices) of the constraints are analysed. It is shown that a change in the objectives may alter the order of their importance. Thus, a change in the strategy should be accompanied with a change in the focus of BSC. Furthermore, non‐financial and financial performance ratios may change in opposite directions, when the strategy is shifted towards revenue maximization. Thus, inconsistencies with the interpretation of cause and effects may emerge, when the strategy is shifted. Numerical examples are presented to demonstrate the results.