Purpose Governments across Asia have increased health spending over the past decade, but child mortality rates – particularly under-five and neonatal deaths – remain high in many countries. The real question is: are countries spending their health budgets efficiently? This study aims to look at how efficiently 42 Asian countries used public health resources between 2010 and 2019 to reduce child mortality, and what factors may explain differences in efficiency. Design/methodology/approach Using a bootstrap data envelopment analysis (DEA), the authors assessed how effectively each country converted health spending into lower child mortality rates. The authors also used bootstrap truncated regression to explore what might drive differences in efficiency – like female labor force participation, access to electricity and out-of-pocket spending by households. Findings Most countries (36 out of 42) showed decreasing returns to scale, meaning more spending did not necessarily lead to better outcomes – just marginal improvements. Only five countries showed increasing returns to scale, where spending was both effective and efficient. The average efficiency improved over the decade, with China and Thailand standing out among emerging economies. In contrast, India had the lowest efficiency score. The analysis also found that social and infrastructure factors, like women’s participation in the workforce and electricity access, played a key role in shaping how efficiently health resources were used. Research limitations/implications This study has few limitations: first, the choice of the time period selected in the study is restricted to pre-Covid-19 period due to data unavailability. Second, because the countries selected in the analysis had to be mapped based on the availability of data, the authors could select only 42 countries. Finally, although, several factors are mentioned in the literature that may improve the efficiency of health expenditures, only a few were included in this analysis to maintain a sizable sample size. Practical implications This study has practical implications, as higher health expenditure may not necessarily translate to better child health outcomes automatically. Rather, what really matters is how well that resources are allocated and used. Governments need to focus on getting better results from the spending they already do. Moreover, such an analysis can provide an understanding of a comparative performance of peers and provide valuable insights into effective policy design, resource management and service delivery. Originality/value This study uniquely links health spending efficiency to health outcomes for a decade across 42 Asian countries using bootstrap DEA.
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.
