Purpose This paper aims to decipher the spatial income convergence pattern in India’s district and statewise. Design/methodology/approach Spatial regression like spatial autoregressivel, SEM, SDM, SDEM and GNS model has been worded out in this. The linear regressions assume the specific assumption about the function form which has origin from Solow growth model. Nonparametric regressions make no assumptions about the functional form of relationships between variables, while random coefficients models extend this approach by allowing the coefficients to vary across groups, capturing heterogeneity at the individual level. Geographically weighted regression (GWR) combines these concepts by incorporating spatial variation, enabling the analysis of how relationships between variables differ across geographic locations. To address spatial heterogeneity, this paper has used GWR, which integrates these ideas by accounting for spatial variation. This approach enables the analysis of how relationships between variables differ across geographic locations, making it particularly useful for studying both conditional and unconditional convergence. Findings This study offers valuable insights into the dynamics of convergence and divergence at both state and district levels in India. It highlights the role of spatial factors and the impact of various variables on the convergence process, shedding light on the nuanced patterns of economic growth and development within the country. Research limitations/implications The significant inequality among Indian districts, alongside evidence of conditional convergence, underscores the urgent need for targeted policy interventions to boost growth in lagging regions. Central initiatives like the “One District, One Product” and the “Aspirational District Program” are vital for addressing disparities. Still, their success depends on effective implementation, monitoring and evaluation at the district level. Complementing these efforts, this paper’s analysis of spatial income convergence highlights the importance of developing alternative growth hubs, such as those under the Counter Magnet Area plan, to ease migration pressure, decongest core regions like Delhi and promote balanced development through improved infrastructure and local economic opportunities in peripheral areas. Social implications The pronounced inequality among Indian districts, coupled with evidence of conditional convergence, highlights the pressing need for tailored policy measures to stimulate growth in underperforming regions. National initiatives like “One District, One Product” and the “Aspirational District Program” play a crucial role in reducing disparities. However, their effectiveness hinges on robust implementation, thorough monitoring and periodic evaluation at the district level. In addition, this paper’s study on spatial income convergence emphasizes the necessity of fostering alternative growth centers, such as those proposed under the Counter Magnet Area plan, to alleviate migration pressures, reduce congestion in central regions like Delhi, and encourage balanced development through enhanced infrastructure and localized economic opportunities in outlying areas. Originality/value This paper’s analysis reveals positive spatial autocorrelation for certain variables, such as gross fixed capital formation, initial GDP levels, health index, per capita power consumption and literacy rate, indicating spatial clustering and the influence of spatial factors. This paper explores both unconditional and conditional convergence, with results suggesting that while income convergence trends are evident, they vary significantly across regions. The financial inclusive index and good health index play crucial roles in driving growth, with positive coefficients indicating their positive influence.
Purpose The purpose of the study is to make an original effort to explore the level of Energy-related financial literacy (ERFL) and its determinants in the context of India’s emerging economy. Design/methodology/approach The study is based on primary data collected from a household-level survey carried out in selected regions of West Bengal. ERFL was measured using the methodology proposed by Blasch et al. (2018). The generalised structural equation model was used to unfold the antecedents of ERFL. Findings The study’s outcome revealed a moderate level of ERFL (63%) among the sample households, primarily driven by a high FL score (71%). Very poor performance was observed in LCCL. EL was also observed to be moderate. The study’s findings highlighted that gender, decision-making power within the family, education, occupation, age, income level and the size of the house were the major antecedents of ERFL and its constituents. Originality/value This article contributes to the small but growing body of scholarship on ERFL. To the best of the authors’ knowledge, this is the first original attempt to explore ERFL and its determinants in the Indian context.
PurposeThis study/paper innovates by modeling household vulnerability through a 50-period dynamic programming set-up, in the backdrop of income uncertainty, subsistence consumption requirements and financial exclusion.Design/methodology/approachTo better understand the roles of various policies in alleviating consumption vulnerability, the authors consider a representative household consisting of a financially excluded single worker with a dependent and earning the minimum wage which is stochastic. Per period borrowing constraints apply and the authors consider a subsistence level of consumption (poverty-line) which the household always attempts to achieve, in the absence of which it suffers a major utility loss.FindingsSimulation results show that financial access is the strongest influencer of mean consumption as well as in reducing household vulnerability (defined as probability of future consumption falling below subsistence). Similarly, subsidized insurance against income shocks is the second most cost-effective policy in alleviating vulnerability. These policies are 8.33 and 2.33 times more cost-effective than a policy which makes uniform income transfers to the household. Other policies like one-time upfront cash payment (impacts early period vulnerability) or policy-maker funded income growth (impacts later-period vulnerability) are also computed to be 1.29 times more cost-effective than uniform income transfer.Originality/valueThis work numerically computes the relative strengths of policies like- financial access, one time upfront cash payment, staggered cash payments and insurance against shocks-in reducing household level vulnerability. To the best of the authors' knowledge, this is the only study which models vulnerability in a multi-period dynamic programming set-up and uses simulations to rank policies with regard to their effectiveness in reducing vulnerability.
PurposeThis study aims to rigorously analyze the admission criteria used by top public business schools in India, utilising the on-campus job placement packages secured by each student. The influence of socioeconomic, demographic and academic variables on employment outcomes in premier public sector business schools in India has been primarily examined.Design/methodology/approachA survey-based approach has been used to collect data from students at public Indian B-schools. A total of 207 responses were received out of 680 requests. The study used statistical techniques to understand the relationship between admissions criteria and placement outcomes.FindingsThe statistical analysis proved that gender did not exert a substantial impact. However, it was observed that female candidates received more generous salary packages than their male counterparts. In addition, the entrance test score, graduation performance, socioeconomic reservation status and possession of an engineering degree have significantly impacted the salary packages received by the students.Research limitations/implicationsThis study is limited to public Indian B-schools only. It can be extended by comparing the international, private and public schools' performances on the same grounds. In addition, the other factors/variables, such as alum status, accreditation status and extra-curricular achievements, can be explored.Practical implicationsThe findings offer significant insights for business schools to consider revising or refining their admission policies to enhance diversity and equity in placement outcomes. Admissions committees can use these data to effectively balance merit-based and socioeconomic factors.Originality/valueThis study presents a distinctive perspective by correlating admission criteria with employment prospects, highlighting sociodemographic and intellectual variables. It enhances the sparse research on admissions and job outcomes in Indian business schools, providing empirical recommendations for policy modifications.
Purpose This study aims to examine the impact of India’s Unified Payments Interface (UPI) on rural financial inclusion and economic growth (2019–2023), focusing on entrepreneurship, household financial behaviour and business formation. It identifies the mechanisms through which UPI adoption drives rural economic transformation. Design/methodology/approach Using a difference-in-differences approach, the study analyses UPI transaction data and household surveys from 200 villages across four Indian states to assess causal effects on financial and economic outcomes. Findings Villages with higher UPI adoption saw a 27% rise in business registrations, a 34% increase in savings accounts and a 42% growth in female-owned enterprises. In comparison, informal borrowing declined by 53% and digital credit access improved by 29%. These effects stemmed from lower transaction costs, better financial information access, network effects driving adoption and increased trust in digital finance. Research limitations/implications The findings are confined to four Indian states – Maharashtra, Karnataka, Uttar Pradesh and Gujarat – and the extrapolation to other regions or emerging economies needs to be taken with caution, observing local economic and institutional conditions. Long-term consequences need to be investigated further. Regional differences, wealth generation and policy reforms applicable to other economies need to be considered in future research. Practical implications Strategic deployment of digital finance, literacy programmes and regulatory safeguards can maximise inclusion and economic impact. UPI adoption also empowers women entrepreneurs, formalises businesses and reduces informal lending dependence, but it requires mitigation of digital exclusion and cybersecurity risks. Originality/value To the best of the authors’ knowledge, this study provides the first causal evidence of UPI’s role in rural economic transformation, introducing a theoretical framework linking digital finance to market integration and financial inclusion.
Purpose This study aims to evaluate the effectiveness and coverage of the Pradhan Mantri Ujjwala Yojana (PMUY) in two districts of West Bengal − South 24 Parganas and Paschim Bardhaman − and to identify the socioeconomic factors that influence the sustained use of liquified petroleum gas (LPG) among PMUY and non-PMUY beneficiaries. Design/methodology/approach This research is based on a primary survey conducted among 576 households across both districts. This study uses comparative analysis and regression techniques to assess PMUY coverage, refill behavior and the determinants influencing sustained LPG use. Findings A total sample of 576 has been collected for this purpose. Distributing to 316 and 260 in the respective districts. The data reveals that PMUY coverage is higher in Paschim Bardhaman areas (82.98%) than in South 24 Parganas (50.85%) counterparts. The Poisson regression result further suggests that, as the age of the household head increases, the probability of refilling LPG cylinders for South 24 Parganas decreases. As household members increase, the likelihood of refilling cylinders increases. Nonetheless, better income, ownership of a house, less reported health issues further enhance the likelihood of refilling LPG cylinders for both areas. Furthermore, a study on willingness to pay has been performed to understand why a percentage of the below poverty line (BPL) population is still dependent on non-clean cooking fuel. The study further depicts that their reservation price is far below the existing price of LPG cylinders, which is further responsible for the absence of the market equilibrium. Therefore, policymakers must emphasize this issue and prescribe further targeted subsidy schemes to ensure clean cooking fuel consumption for these marginalized sections of the population. Research limitations/implications One key limitation of this study is the relatively small sample size, which was constrained by time and budget limitations. Due to these constraints, the analysis was limited to a subset of households, which may not fully capture the diversity and complexity of the broader population. A larger sample size involving more households would allow for a more comprehensive and generalizable understanding of the phenomena under investigation. Future research should aim to expand the scope of data collection to include a wider range of households, thereby enhancing the depth and reliability of the findings. Practical implications The findings of this primary survey on the PMUY offer important practical implications for improving the scheme’s implementation and effectiveness. This study identifies key factors that influence the adoption and sustained use of LPG in terms of affordability of refills, awareness of health benefits and accessibility of distribution points. These insights can guide policymakers, implementing agencies and local authorities in refining strategies to increase refill rates, enhance beneficiary support and strengthen last-mile delivery. Social implications This research provides valuable insights into the social impact of the PMUY, highlighting both its effectiveness and areas for improvement based on primary data from beneficiaries. The findings can inform policymakers about the ground-level realities of implementation, helping to enhance outreach, ensure sustained usage and address challenges such as refill affordability and awareness. Ultimately, this research supports efforts to promote cleaner energy use, reduce indoor air pollution and improve the quality of life for the BPL households across India. Originality/value This research is original in every aspect of its design and execution. The questionnaire was specifically developed for this study, reflecting the unique objectives and context of the research. The sample selection process was carefully planned to align with the study’s aims, ensuring relevance and accuracy. Data collection was conducted independently, adhering to rigorous standards to maintain authenticity and reliability. Furthermore, the data analysis was carried out using methods tailored to the specific characteristics of the data set, providing novel insights. Overall, this research presents a fully original and methodologically sound contribution to the field.
Purpose The purpose of this study is to assess the adequacy of houses constructed under the PMAY-G scheme in selected mining-vulnerable areas within Paschim Bardhaman District, specifically investigating the program’s effectiveness in addressing the area’s susceptibility. Design/methodology/approach Employing a mixed research approach, this study evaluates the qualitative adequacy of housing using a questionnaire survey conducted in both vulnerable and non-vulnerable zones. A five-point Likert scale is used to gauge housing adequacy with Descriptive Statistics serving as the primary analytical tool. Findings The findings revealed an overarching deficiency in housing across various components, highlighting the precarious living conditions that are prevalent in these areas. Most respondents perceived that the houses provided under PMAY-G are qualitatively inadequate in mining areas compared with non-mining areas. Research limitations/implications These results offer valuable insights for evaluating the efficacy of low-cost affordable public housing in the study area, and suggest measures to enhance the qualitative adequacy of future housing construction. Originality/value This study contributes to the scant literature on the PMAY-G scheme in the context of housing adequacy in the mining zone of India. This analysis may help identify possible sustainable development resolutions and assist policymakers in developing integrated sustainable development strategies.
PurposeThe purpose of this study is to examine how infrastructure development contributes to economic growth and income inequality in India using annual data from 1990 to 2022.Design/methodology/approachThe study applies the Autoregressive Distributed Lag (ARDL) model proposed by Pesaran et al. (2001) to estimate both the short-run and long-run growth impact of infrastructure development and other regressors. The Zivot-Andrews test is used to identify the structural break in the data. The Granger causality test is used to examine the direction of causality between infrastructure development and economic growth. A time series regression model is used to assess the impact of infrastructure development on income inequality.FindingsThe study presents several key findings. Firstly, the bounds test confirms the presence of long-run cointegrating relationships between economic growth and its regressors, including infrastructure development. Secondly, infrastructure development exerts a significant positive impact on economic growth both in the short and long run. Thirdly, the causality test indicates bi-directional causality between infrastructure development and economic growth. Fourthly, government expenditure has a negative and significant impact on economic growth in the long run. Fifthly, regression results indicate that infrastructure development plays a significant role in reducing income inequality by enhancing access to essential services. Sixthly, government expenditure and economic growth have no significant impact on income inequality in India.Practical implicationsThis study offers empirical insights into how infrastructure development contributes to economic growth and income inequality in India. As India pursues its ambition of becoming a developed nation by 2047 and fulfilling its commitments to reduce income inequality (SDG-10), the findings underscore the importance of scaling up investments in infrastructure, an area where India still lags behind compared to developed nations and many emerging economies. The study emphasized the need for enhancing public spending on infrastructure development as a critical policy tool for achieving sustained economic growth and reducing inequality in line with Sustainable Development Goal-10 (SDG-10).Originality/valuePrevious research mainly concentrated on the growth impact of infrastructure development. There is a notable scarcity of literature assessing the impact of infrastructure development on income inequality in India. This study aims to address this research gap by investigating the impact of infrastructure development on income inequality in the context of the Indian economy. To the best of the author's knowledge, this paper represents the first attempt to empirically study the impact of infrastructure development on income inequality. It contributes significantly to the existing knowledge by providing compelling evidence of the positive impact of infrastructure development on economic growth and income inequality in India.
PurposeThis study aims to analyse the status of economic inequality and economic mobility in Punjab through the lens of agricultural and non-agricultural household income.Design/methodology/approachThis paper uses longitudinal data from the India Human Development Survey (IHDS) for the periods 2004-2005 and 2011-2012. It compares agricultural and non-agricultural household income across different income quintiles, explores income inequality using the Gini coefficient and Lorenz curve, and measures mobility through a transition matrix among these households with varying socio-demographic characteristics.FindingsThe study found that inequality increased for both agricultural and non-agricultural households over the period, with Gini coefficients increasing to 0.58 and 0.47, respectively. However, agricultural households were the primary contributors to rising inequality at the state level. For agricultural households, the primary drivers of inequality were "Others" (General) category households and female-headed households. For non-agricultural households, the "Others" category households and male-headed households were the main sources of inequality. The highest economic immobility among agricultural households was contributed by the "Others" category and male-headed households, while mobility among non-agricultural households was driven by SC and OBC households and male-headed households.Originality/valueBy examining inequality and mobility among agricultural and non-agricultural households, this study can determine whether one sector, both sectors or specific caste or gender categories require more policy focus to enhance economic growth in Punjab. Moreover, the literature lacks studies that explore economic mobility through agricultural and non-agricultural income in Punjab. This study can provide valuable insights into economic inequality and economic mobility in the context of Punjab.
Purpose This study aims to understand the relationship between extension services, crop types and farmers’ market participation in India. It further explores the factors influencing farmers’ choice of market and the role of extension service in shaping their decision of market participation. Design/methodology/approach The multivariate logistic regression and probit model are used to understand factors influencing the farmers’ choice of market participation. Findings This study finds that the major crop selling takes place through the non-regulated markets. This highlights the crucial role of non-regulated spaces in the Indian agricultural marketing system, in spite of their shortcomings. The same pattern can be observed across all crop categories, indicating that institutional market mechanisms are either inaccessible or inefficient for the majority of the farmers. The study further reveals that the private extension services have emerged as a key and widely relied upon source of market-related information. The results show that farm households mainly rely on private extension services for agriculture-related advice. The logistic regression shows that demographic, farm and institutional variables also significantly influence farmers’ choice of market channels. The findings offer managerial implications by advocating for a dual market approach that improves transparency in non-regulated markets while addressing their inherent inefficiencies. This study also advocates for the strengthening of extension services to enhance farmer participation. Originality/value This study uniquely links extension services and crop choices to farmers’ market participation, offering fresh evidence from India’s unit-level data of the latest round (77th round, 2018–19) of the National Sample Survey on agricultural households in India.
PurposeThis paper aims to examine whether firms adopting lean manufacturing practices experience higher total factor productivity (TFP) across formal, private Indian manufacturing firms. Moreover, this paper examines a broad set of determinants influencing lean adoption, considering both internal firm attributes such as size, export orientation, research and development(R&D) activity and management quality, as well as external constraints like electricity reliability and exposure to competition from the informal sector.Design/methodology/approachThis paper uses nationally representative data from the 2022 World Bank Enterprise Survey for India. Firm-level TFP is estimated using a Cobb-Douglas production function based on a gross output specification. To examine the predictors of lean adoption, this paper uses a logistic regression model.FindingsThe findings indicate that lean practices remain limited in the broader landscape of Indian manufacturing, with only 3.60% of firms reporting lean adoption. However, adopting firms exhibit higher TFP, underscoring the potential productivity benefits. Firm-level characteristics such as size, ownership, foreign-licensed technology use, training provision and management quality are vital enablers. Notably, firms with foreign ownership or those operating in environments marked by frequent power outages and intense informal competition are less likely to implement lean systems.Originality/valueThis paper makes an original contribution by exploring how lean manufacturing influences firm-level productivity in India while identifying structural barriers to its adoption. Unlike previous research, it emphasises underexplored factors such as infrastructure deficits and institutional constraints, which are particularly relevant in the Indian context. The paper provides new empirical insights into how both internal firm characteristics and external environmental factors jointly shape lean adoption.
PurposeThe purpose of this paper is to examine the complex relationship between agricultural input subsidies and environmental sustainability in India. It specifically looks at how fertilizer, power and irrigation subsidies affect the environment and compares how much they contribute to greenhouse gas (GHG) emissions from farming activities.Design/methodology/approachThe study utilizes an autoregressive distributed lag model within a time-series framework, examining data from 1981 to 2020. The model delineates both the short-term and long-term interactions between input subsidies and GHG emissions, thereby quantifying the degree to which subsidized inputs exacerbate environmental externalities.FindingsThe results show that subsidies for fertilizer and power greatly raise emissions, with the power subsidy having the most intense emissions. The irrigation subsidy, on the other hand, has no effect on emissions. These results show that different parts of the subsidy have different effects on the environment. They also show the tradeoffs between making farming more productive and keeping the environment healthy.Research limitations/implicationsThis study extends the subsidy-productivity framework by empirically linking input subsidies to greenhouse gas emissions, thereby illustrating a rebound effect akin to the Jevons Paradox in Indian agriculture. It highlights the need to incorporate environmental dimensions into existing theories that have traditionally focused only on yield enhancement and farmer welfare.Practical implicationsThe findings underscore the need to redesign subsidy programs by gradually shifting away from inorganic fertilizer subsidies toward cash transfers that incentivize organic and low-carbon alternatives. Policymakers should integrate agri-environmental schemes, promote renewable energy use in farming and provide farmer training on climate-smart practices to align agricultural productivity goals with long-term sustainability.Originality/valueThis research provides original insights by measuring the varying emission intensities of agricultural input subsidies in India. The study provides a comprehensive framework by combining empirical evidence with policy implications, facilitating the formulation of environmentally sustainable agricultural policies that mitigate emissions while maintaining productivity.
PurposeThe purpose of this study is to understand the impact of conditional cash transfers (CCT) and health advice (HA) provided under India's Janani Suraksha Yojana (JSY) on child health outcomes, specifically neonatal and infant mortality (NMR and IMR) rates, in India's low-performing states. By analysing data from the National Family Health Survey (NFHS), the authors aim to analyse the individual and combined impact of CCT and HA on improving child health, particularly for children of mothers with low education, from disadvantaged castes and with lesser wealth.Design/methodology/approachThis research analyses data from the NFHS 2015-2016 (NFHS-4) and 2019-2021 (NFHS-5) to examine the effects of CCT under the JSY and HA on child health outcomes in public health-care settings. The methodology involves using descriptive statistics, regression models and sub-group analyses to compare NMR and IMR among different socio-economic groups. This study also explores variations by caste, education and wealth, assessing the impact of CCT and HA on enhancing child health outcomes.FindingsThis study finds that CCT under JSY significantly reduce NMR and IMR, especially when combined with HA. While CCT alone improves child health outcomes, HA alone does not show a significant effect. The combined impact of CCT and HA is most effective for children of mothers with low education, from disadvantaged castes and lower-income families. However, a decline in CCT coverage from 2015-2016 to 2019-2021 suggests missed opportunities for improving child health outcomes, highlighting the need for better HA delivery and expanded CCT reach.Originality/valueThis study contributes original insights by differentiating the impacts of CCT and HA on child health outcomes within India's JSY. Unlike existing research, it distinctly evaluates the separate and combined effects of CCT and HA, demonstrating the enhanced effectiveness of their integration. This research emphasises the significance of targeting CCT and HA to the most disadvantaged socio-economic groups to optimise health outcomes. This nuanced analysis provides valuable implications for policymakers, suggesting enhancements in the delivery of health services and the structuring of maternal assistance programmes to better address inequality and improve child health.
Purpose - Crop diversification can be adopted as a strategy to make efficient use of resources and overcome region-specific constraints faced by smallholder organic farmers in the mountainous state of Sikkim in Northeast India. Facilitated by favourable agro-climatic conditions, diversification towards high-value crops can benefit a large number of farmers in this state. This paper aims to identify the factors influencing crop diversification and its extent in Sikkim, India. Design/methodology/approach - Using primary data, collected from 170 organic farmers in two districts of Sikkim, this study has identified the determinants of households' decision to diversify crops and the level of crop diversification. Heckman two-stage model along with Simpson Index of Diversification (SID) has been used for the analysis. Findings - The results show that crop diversification has been adopted by about 46% of the households in the study area. The major factors which determine the household's decision to diversify crops are age, education, farming experience, training, irrigation, membership in a farmer producer organisation (FPO) and distance to the nearest town. A household's level of diversification is majorly influenced by farming experience, number of cattle owned, irrigation and membership in an FPO. Research limitations/implications - This study was based on a cross-sectional primary survey. Therefore, the findings may be subject to some limitations even though all efforts were made to minimise them. Originality/value - The study is based on a novel data set collected to examine the determinants of crop diversification in the hilly state of Sikkim in Northeast India. The study extends the growing body of literature on the inherent prospect of crop diversification and farmers' adaptations towards the changing climatic conditions in the hill economy.
PurposeThis study aims to assess the performance of leather tanneries in India by measuring their technical efficiency (TE) and identifying the impact of firm size and other factors that may be responsible for variations in efficiency.Design/methodology/approachThis study uses cross-sectional data from five consecutive years (2017-2018, 2018-2019, 2019-2020, 2020-2021 and 2021-2022) provided by the Annual Survey of Industry (ASI) to fit the Cobb-Douglas-type stochastic frontier function. In addition to estimating the frontier function, an inefficiency model is also used to assess the impact of the firm's size and other determinants.FindingsThe result of this study shows that in the current economic scenario, the Indian leather tanneries are working at an average efficiency level between 72% and 80%, spanning over the sample years. The significant coefficient of labor and intermediate inputs indicates that the leather industry is labor-intensive, whereas capital has an insignificant contribution in determining production behavior. Furthermore, the size of the firm has a positive impact on the firm's efficiency, and there is no clear relationship between the age of the firm and its level of TE.Research limitations/implicationsThis study only uses data from registered firms, as the ASI exclusively surveys registered manufacturing firms in India. In addition, this study is based on cross-sectional data from each of the five years, and some qualitative factors - such as owner education, managerial practices, innovation strategies or marketing efforts - are absent from the ASI data, limiting the ability to explain variations in efficiency.Originality/valueThe unique aspect of this study is its utilization of a five-digit National Industrial Classification (NIC) code to identify companies in the leather tanneries from the ASI. Previous studies on the leather industry have not used this approach, as they have typically used three-digit or four-digit NIC codes, only measuring TE at the overall industry level. In addition, this study uses two proxies to gauge the size of the firm and evaluate their impact on the firm's efficiency.
PurposeTribal farmers in India are among the most vulnerable societal groups affected by climate change and variability. This study aims to examine the perceptions and adaptation strategies of tribal farmers in response to climate change in the Kandhamal district of Odisha, India.Design/methodology/approachThis research is based on cross-sectional data collected in 2024 from 150 tribal farmers using semi-structured questionnaires and focus group discussions. The data were analyzed using descriptive statistics and a logistic regression model.FindingsThe findings indicate that nearly all tribal farmers identified various signs of climate change: 90.2% acknowledged that the climate has changed, 85% reported irregularities in rainfall patterns, 87.25% observed an increase in temperature and 55.33% noted a rise in winter rainfall. The farmers' perceptions of climate change are significantly influenced by several factors. Gender, age, education level, agricultural training, climate awareness, farming experience, availability of irrigated land, income level, crop insurance and access to loans are among the factors listed.Research limitations/implicationsFuture research should focus on integrating indigenous knowledge with scientific advancements to develop holistic climate adaptation frameworks with the coverage of more states to give a clear picture about the perception and adaptation of climate change of tribal farmers.Originality/valueMost studies in India focus on different states or regions, but to the best of the authors' knowledge, very few examine tribal farmers' adaptation and perception of climate change, especially in Odisha, one of the most vulnerable and second-highest tribal-populated states.
PurposeThe purpose of this paper is to examine how visible inequality affects household consumption choices in India, specifically the trade-off between essential food expenditure and conspicuous consumption. The aim is to identify whether rising inequality places a burden on households by pushing them toward status-driven spending at the expense of nutrition.Design/methodology/approachThis study uses data from the latest Household Consumption Expenditure Surveys (HCES 2022-2023 and 2023-2024). Following Friehe and Mechtel (2014), conspicuous consumption is defined by mapping the relevant expenditure categories from the HCES data. Visible inequality is measured as the leave-one-out standard deviation of conspicuous consumption at the First Sampling Unit level. To address endogeneity, a two-stage least squares approach is used, using the Theil index of monthly per capita expenditure as an instrument. Regression models with district fixed effects and sampling weights are estimated for robustness.FindingsThe results of this study show that visible inequality has a positive and significant association with conspicuous consumption and a negative association with food consumption. Households allocate a larger share of their expenditure to conspicuous goods when inequality rises, while reducing spending on essential food items. The social pressures of status signaling may be one of the reasons that distort household consumption priorities.Research limitations/implicationsThis study uses objective measures of inequality and visibility, but it cannot directly capture perception-based or psychological dimensions of status comparison. The data set also lacks detailed information on the frequency and visibility of specific conspicuous items. Future research can use perception-based surveys for further understanding of these mechanisms.Practical implicationsThe findings of this study suggest that as inequality increases, households may prioritize status goods over essential consumption. This has important policy implications, as rising inequality can negatively affect nutritional well-being. Therefore, policymakers should consider inequality when designing policies to improve nutritional outcomes.Social implicationsBy diverting spending from food to status goods, visible inequality may worsen nutritional outcomes. Understanding this association is important for policies aimed at improving welfare, reducing poverty traps and promoting equitable development.Originality/valueTo the best of the authors' knowledge, this is among the first empirical studies in India using the latest nationally representative HCES data to examine the effect of visible inequality on household consumption trade-offs. This study provides robust econometric evidence on the role of social comparison in driving conspicuous consumption and highlights its consequences for food security and welfare.
Purpose The purpose of this paper is to analyze consumers’ preferences for alternative fuel vehicles, that is, battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs) and hydrogen fuel cell vehicles (HFCVs). In addition, in alignment with the Government of India’s faster adoption and manufacturing of electric vehicles (FAME) scheme, the paper evaluates the impact of subsidies for BEVs on shaping consumer preferences. Design/methodology/approach This study conducted a survey in August 2024 from two districts of Delhi, India – New Delhi and North West Delhi. A discrete choice experiment was designed, presenting respondents with four alternatives – battery electric vehicles plug-in hybrid electric vehicles, hydrogen fuel cell vehicles and conventional gasoline vehicles. This paper estimates conditional, random parameter and latent class logit models to estimate consumers’ willingness to pay for alternative fuel vehicles under no-subsidy and subsidy scenario. Findings The results show that consumers are willing to pay 1,275,000 rupees for BEVs, 1,239,000 rupees for PHEVs and 1,073,000 rupees for HFCVs. Attributes such as price, battery charging station, charging time are strongly valued. Also, subsidy for purchasing alternative fuel vehicles is an effective measure to increase willingness to pay for alternative fuel vehicles. Originality/value To the best of the author’s knowledge, this is the first paper to estimate consumers’ willingness to pay for alternative fuel vehicles in Delhi, India and examine how this willingness is influenced when a subsidy is introduced.
Purpose While rural–urban migration is a universal phenomenon and people migrate for various reasons, the process of migration can have its effect on the left-behind family and the place of origin. The effect of such migration on the place of origin may be either positive or negative, depending on the amount of remittances sent by the migrant workers back home, type of activities on which these remittances are used and availability of labour back home, etc. The New Economics of Labour Migration (NELM) theory asserts that labour migration may have a negative effect on farm productivity initially through labour loss effect, which can be overcome through the inflow of remittances and their proper utilisation. This study aims to investigate whether rural–urban labour migration in Assam lends support to the theory of NELM or not. Design/methodology/approach This study is entirely based on primary data collected from 284 households from four districts of Assam in Northeast India. These districts are selected based on parameters, such as district-wise share of cultivators in the total workforce, district-wise contribution of agriculture and allied activities in district gross domestic product and district-wise percentage of migrant households. The study makes use of the three-stage least squares (3-SLS) method to jointly determine the factors influencing migration and remittances and their impact on the farm productivity of different farm sizes. Findings The results of the study show that remittances received by the households have a positive impact on farm productivity, whereas migration has a negative impact. This suggests that the negative effect of migration on productivity is offset by the remittances received by the households. The findings of the present study lend support to the NELM Theory, which states that migration results in productivity loss in agriculture by way of reduction in family labour. However, the inflow of remittances has its positive impact by enabling farm households to use hired labour to compensate for the loss of family labour in agriculture, which finally results in increased land productivity. The study has also found that the impact of remittances on productivity is higher among the relatively large farm holders compared to small land holders. Research limitations/implications The major limitation of the study relates to the size of the sample taken up. The present study was carried out with the help of primary data collected from four districts in Assam. However, as there was no previous data to identify a migrant, we had to rely on snowball sampling. The process does not allow one to estimate the total number of migrants in the districts (population). Thus, it was not possible to determine the size of the sample with an ideal statistical formula. We had to rely on judgement rather than on any statistical formula to decide on the sample size. Originality/value The study examines the impact of migration and remittances on agricultural productivity in Assam with reference to varying land holding sizes with the help of the method of 3-SLS, which has not been done by earlier studies.
Purpose This paper aims to analyze how economic policy uncertainty (EPU) affects inflation expectations. The study uses unit-level observations of 3 months ahead and 1-year ahead inflation expectations from the Reserve Bank of India’s Inflation Expectations Household Survey (IESH) to explore the variations in consumer confidence across several cities in India. Design/methodology/approach This paper uses unit-level observations of 3 months ahead and 1 year-ahead inflation expectations of the households from the Reserve Bank of India’s IEHS between September 2008 to July 2024. It uses a fixed effects regression exercise to examine the impact of EPU on the households’ inflation expectations. Findings The study results show that EPU negatively and significantly impacts consumers’ short-term inflation expectations. Heterogenous analysis concerning gender reveals that impact of EPU on inflation expectations is higher for women than men. Further, respondents in the working-age population and greater income uncertainty are more sensitive to changes in EPU on inflation expectations. A disaggregated analysis is also conducted to analyze the impact of EPU on several disaggregated indicators of inflation expectations, showing that EPU has a larger impact on durables and house prices compared to nondurables and services. Robustness tests with alternative policy uncertainty measures and placebo tests corroborate our findings. Practical implications These findings indicate that EPU can potentially depress the household’s own price expectations related to several components of their consumption basket due to precautionary saving behavior, consequently leading to a pessimistic view of overall prices. Originality/value To the best of our knowledge, no study has explored the impact of inflation policy uncertainty on inflation expectations using unit-level survey data for an emerging economy like India. Furthermore, survey-level data allows us to examine the heterogenous impact of EPU based on household characteristics. Moreover, it also establishes the household’s own consumption basket channel through which EPU impacts the overall inflation expectations.
