It is crucial to understand how environmentally friendly investments are interrelated with commodity futures. This study examines the quantile connectedness among global environmental, social and governance (ESG) stocks and commodities using the quantile vector autoregression (QVAR) approach on data spanning from May 2017 to April 2024. The findings exhibit higher return spillover in lower and upper quantiles and less spillover in the median quantile. Furthermore, during the COVID-19 pandemic and the Russia–Ukraine war, connectedness has increased among ESG stocks and commodities in all market conditions. Further analysis indicates that Europe ESG leaders (EU_ESG) and USA ESG leaders (US_ESG) serve as diversifiers in all market scenarios, but world ESG leaders (WL_ESG) act as a diversifier just in the natural market. Emerging markets ESG leaders (EM_ESG) generally function as a risk absorber and, in calm times, act as a hedge against developed ESG shocks. Finally, gold functions as a safe haven in crises for environmentally friendly investments. Our findings provide valuable information to investors, policymakers and portfolio managers regarding portfolio diversification. JEL Classifications: O16, C58, C61, G41

An empirical investigation has been conducted in the present study to observe the climate-economy interactions of Indian states and Union Territories from various dimensions over the period from January 2010 to December 2023. Thus, monthly average precipitation and temperature have been taken into consideration as climate variables, and the average gross state domestic product (GSDP) as the economic variable. For empirical inference, multiple regression equations and panel regression equations have been developed and estimated. Precipitation has a significant positive impact on average regional GSDP, whereas the North-East region has shown a significant negative temperature shock. Moreover, the seasonal precipitation shock has a stronger effect on the regional average GSDP compared to temperature. Additionally, panel estimation has revealed that both temperature and precipitation have a significant adverse effect on average seasonal GSDP. JEL Classifications: Q54, Q56, Q58, C21

The current research applies a multi-stage approach for measuring and explaining the efficiency performance of 20 general insurers operating in India for the phase 2012–2013 to 2019–2020. In the first stage, the study adopts non-parametric radial data envelopment analysis (DEA) for point and interval estimation of firm-specific efficiency, scale efficiency, returns to scale (RTS) and scale elasticity. The second stage of the study applies panel data regression for regressing technical and scale efficiency scores on the index of market concentration, insurer age, return on shareholders’ capital and the solvency indicator. The outcome of the next stage indicates that the index of market concentration and insurer age are the two contextual variables which are statistically significant, although their impacts on technical and scale efficiency are negative. The influence of the solvency ratio is significant for scale efficiency only. JEL Classifications: C-23, C-61, D-22, G-22

Research papers on female labour force participation (FLFP) are neither rare nor uncommon. However, papers measuring unpaid female labour are not many. This paper estimates the increase in gross domestic product (GDP) due to an increase in FLFP using two rounds of data from the Periodic Labour Force Survey (PLFS) of India. To show the effect of including female workers who are out of the labour force on the national income of the country, I adopt the method of imputing incomes for women not in employment, accounting for two scenarios: (a) without work and without a child and (b) without work and with a child. The estimates from our sample suggest an increase of 31% in total income earned if women who are out of the labour force are employed gainfully. Moreover, increasing participation of women aged 36–55 could make a substantial contribution towards reducing the gap between per capita income of India at present and the target it has set to achieve. JEL Classifications: J01, J21, J22, J12

Despite the transformative effects large language models are having on education, the current economic models of student cheating do not factor in the impact of a highly capable artificial intelligence (AI). We attempt to fill this gap by constructing an economic model of student effort, course difficulty and cheating using AI on a per-course requirement basis. We find that to maximize student knowledge, there are two viable approaches to instructor selection of course difficulty. The ‘carrot’ approach is characterized by low course difficulty to incentivize effort with a high grade, while the ‘stick’ approach is characterized by high course difficulty to motivate enough effort to pass the course. We also find that student effort falls as the capability of AI increases. As a result, AI can eliminate the strategy of using high course difficulty to motivate students as a viable pedagogical option beyond a certain threshold level of AI capability. JEL Classifications: I21, I23, A22

In the measurement of multidimensional deprivation, dimensions of deprivation are often weighted by people using either their own implicit value judgements or data-based statistical methods. Each of these methods has limitations. The weights based on implicit value judgements leave no scope for any reason-based public scrutiny so that, over time, a consensus may develop over a set of value judgements on which the weighting of dimensions ought to be based. The data-based statistical methods also seem to be inadequate as they ignore the distinction between ‘is’ and ‘ought’, whereby what ‘ought’ to be cannot be derived from what ‘is’. In this paper, we argue for an approach to determining the weights of the dimensions of deprivation based on normative judgements. We adopt clearly spelt-out, norm-based weighting. The dimensions of deprivation in our analysis are categorized into basic and non-basic dimensions based on human rights and the hierarchy of needs theory. The basic dimensions of deprivation are given more weight than the non-basic dimensions of deprivation. We construct a class of norm-sensitive deprivation indices based on Jayaraj and Subramanian (2010) index that falls in the similar axiomatized group of indices of Chakravarty and D’Ambrosio (2006) . We provide some applications of these indices using India’s National Family Health Surveys. We observe certain significant changes in the deprivation ranking of the Indian states as compared to the other methods followed in the literature. JEL Classifications: D63, I31, I32

In network games under a cooperative framework, the position value is a link-based allocation rule. It is obtained from the Shapley value of an associated cooperative game where the links in the network are considered players. The Shapley value of each of the links is then divided equally among the players who form those links. The inherent assumption is that the value is indifferent to the weights of the players in the network. Depending on how central a player is in the network or their ability to form links with other players, etc., players can be considered to have weights. Thus, in such situations, dividing the Shapley value equally among the players can be an overly simplistic notion. We propose a generalized version of the position value, that is, the weighted position value, that allocates the Shapley shares proportionally to the players’ weights. These weights for the players are exogenously given. We provide two axiomatic characterizations of our value. Finally, a bidding mechanism is formulated to show that any subgame-perfect Nash equilibrium (SPNE) of this mechanism coincides with the weighted position value. JEL Classifications: C71, C79

The ecological footprint (EFP) is an important measure reflecting the interaction between humans and the environment. It provides the requirements to absorb the waste and emissions generated by humans in terms of pressure on natural resources. Therefore, an accurate prediction of EFP is vital to develop an understanding of sustainable development, the ecosystem, environmental protection, and resource utilization, especially in India, which has one of the highest total ecological deficits. This study applies various machine learning (ML) models to predict EFP in India based on 11 potential predictors covering trade openness (TO), urban population (UP) and renewable and fossil-fuel energy consumption over the period 1980–2017. The results show that the Random Forest (RF) model generates the lowest errors for prediction among the considered models and that five variables, namely inflation, renewable energy consumption (REC), role of primary sector in the economy, UP and human capital (HC), are the most crucial predictors of EFP. JEL Classifications: C19, D70, P18, Q59

The Indian hotel and restaurant (H&R) industry is up against fierce competition from large hotels and the rapidly expanding online market, particularly in the aftermath of the epidemic. This research employs data envelopment analysis (DEA) to evaluate the impact of COVID-19 on relative efficiency of 265 H&R firms in India. The analysis reveals substantial operational and scale efficiency (SE) discrepancies, underlines exemplary enterprises that could serve as benchmarks for underperforming firms, and offers a discerning evaluation of operational and strategic management modifications. Moreover, underscored by slack analysis are significant inefficiencies in resource usage, which are aggravated by increasing competition from overseas hotel brands and online food delivery firms. The study recommends that H&Rs optimize workforce management, review staffing needs and apply takeout services to increase efficiency. The analysis of slack indicates considerable inefficiencies in capital allocation, asset management and operational cost regulation. The findings offer strategic insights for management strategies, emphasizing the need for scale optimization, workforce rationalization and technology integration. Furthermore, by implementing strategic interventions, the government can play a significant role in industry revival, efficiency enhancement and long-term sustainability in a highly competitive market. JEL Classification: B21, C61, D24

The paper endeavours to assess the export competitiveness of the textiles sector of India. This is followed by an inter-regional comparison of the comparative advantage of the handloom sector. At the end, the major macro-level determinants of the comparative advantage are identified. Analysis reveals export competitiveness of textile products from India. The results of inter-regional comparison of export competitiveness among five different regions of the country indicate comparative advantage in the case of the North Indian and North East Indian regions of India. A negative correlation is identified between per capita state GDP, gross capital formation, number of factories with export competitiveness as measured by RSCA. This paves the way for further research to explore the impact of the relevant macroeconomic factors on export competitiveness which is so far unexplored in the context of handloom sector. JEL Classifications: F14, L67, F1, Q1