District Level In India Research Articles

District-level monitoring of universal health coverage, India.

To develop a framework and index for measuring universal health coverage (UHC) at the district level in India and to assess progress towards UHC in the districts. We adapted the framework of the World Health Organization and World Bank to develop a district-level UHC index (UHC d ). We used routinely collected health survey and programme data in India to calculate UHC d for 687 districts from geometric means of 24 tracer indicators in five tracer domains: reproductive, maternal, newborn and child health; infectious diseases; noncommunicable diseases; service capacity and access; and financial risk protection. UHC d is on a scale of 0% to 100%, with higher scores indicating better performance. We also assessed the degree of inequality within districts using a subset of 14 tracer indicators. The disadvantaged subgroups were based on four inequality dimensions: wealth quintile, urban-rural location, religion and social group. The median UHC d was 43.9% (range: 26.4 to 69.4). Substantial geographical differences existed, with districts in southern states having higher UHC d than elsewhere in India. Service coverage indicator levels were greater than 60%, except for noncommunicable diseases and for service capacity and access. Health insurance coverage was limited, with about 10% of the population facing catastrophic and impoverishing health expenditure. Substantial wealth-based disparities in UHC were seen within districts. Our study shows that UHC can be measured at the local level and can help national and subnational government develop prioritization frameworks by identifying health-care delivery and geographic hotspots where limited progress towards UHC is being made.

Which Nighttime Lights Data Better Represent India’s Economic Activities and Regional Inequality?

This paper demonstrates how the estimation of economic activities and inequality for the Indian economy based on nighttime lights data from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite outperforms those obtained using traditional Defense Meteorological Satellite Program Operational Line-Scan System (DMSP-OLS) sources. In the extant literature, nighttime lights data have been frequently used as a proxy for economic activities, especially at granular geographical levels where gross domestic product data are otherwise unavailable. Since the traditional DMSP-OLS-based nighttime lights data suffer from measurement errors due to blurring and top coding, VIIRS-based nighttime lights data, which are devoid of such limitations, are gaining popularity. We show that at the subnational or district level in India, VIIRS satellite-based nighttime lights data are indeed a better proxy for economic activities and inequality. Our robust findings imply that all future scholarly works that employ nighttime lights data for the Indian economy should use VIIRS-based data for greater accuracy compared to other similar data sources.

District-level epidemiology and sociodemographic determinants of noncommunicable diseases - results the National Family Health Survey −5 (2019–21)

BackgroundNoncommunicable diseases (NCDs) are the leading cause of adult mortality in India. However, the data regarding the prevalence of NCD risk factors at district level is scarce. This study aims to analyse and map NCD risk factors at the state and district levels, exploring sociodemographic influences on these risks in Indian males and females. MethodsWe analyzed National Family Health Survey-5 database and used the prevalence estimates to create choropleth maps, enabling us to examine the geographical variations in NCD risk factors at the district level in India. ResultsDistricts in the Satluj-Yamuna plains, western Rajasthan, and the northeastern regions exhibited clusters with a prevalence of high blood pressure exceeding 30.1 %. Northeastern districts showed over 40 % prevalence of current tobacco use, while high alcohol consumption clusters were observed in the northeastern and Telangana districts. Southern districts showed clusters of both obesity (as measured by BMI) and highest rates of oral, breast, and cervical cancer screening, moreover districts in Tamil Nadu exhibited notable clusters of raised blood glucose prevalence. ConclusionOur analysis revealed variations in the prevalence of NCD risk factors at both the state and district levels. Accordingly, this study ranks districts based on the NCD burden, offering valuable insights to state and district teams to devise targeted measures for the prevention and control of NCDs, particularly in the most heavily affected districts.

Agglomeration Economies and Rural-to-Urban Migration in India

<p style="text-align:justify"><span style="font-family:Times New Roman,Times,serif"><span style="font-size:16px"><span dir="ltr" lang="EN-US"><span style="color:black">Keeping in view the concept of agglomeration economies and the New Economic Geography (NEG) angle, this paper makes an attempt to examine the rural to urban population movement at the district level in India. The findings do not favour a strong positive association between levels of urbanization and migration rates.  However, there exists a cluster of districts which are able to attract migrants on a large scale in spite of being already urbanized. The work participation rate, share of services and construction work, and literacy rate all form parts of this positive nexus, indicating that opportunities exist with increased levels of urbanization which in turn prompt people to migrate. The positive spill-over effects of higher levels of urbanization are not limited to the urban spaces only as the adjoining rural areas also seem to have undergone   a significant transformation. The land use pattern and activities are changing and some of the developmental impact is evident. However, the regional variations in this respect are evident. There are sharp differences in the relationship between urbanization level and migration rates across regions. The nature of urbanization and its determinants and the outcome variables of urbanization and migration also unravel regional variations. Besides, there are many districts with higher levels of urbanization; yet, they are not able to attract migrants at a rapid pace. New investment opportunities can be created in these spaces to reduce the cost of growth and make employment generation process more effective, facilitating the rural population to take the benefits of agglomeration economies.</span></span></span></span></p>

Addressing multicollinearity in spatial modelling: A district level spatial analysis of pandemic COVID-19 in India.

This study focuses on conducting spatial analysis of COVID-19 at the district level in India. Leveraging data from www.covidindia.org for confirmed cases and deaths, and integrating population characteristics from the National Family Health Survey 5 (2019-2021) and supplementary sources. The objective is to identify risk factors using spatial modelling techniques while addressing multicollinearity through principal component analysis (PCA). This study utilizes spatial analysis to identify COVID-19 hotspots and coldspots at the district level in India. It highlights highly affected districts such as Mumbai, Pune, Chennai, Kolkata, and Bengaluru, as well as low affected districts in central and north-eastern regions. The study utilized the spatial lag model (SLM), spatial error model (SEM), geographical weighted regression (GWR), and multiscale geographical weighted regression (MGWR) models to analyse the impact of demographic, socioeconomic, climatic, and comorbidity factors on COVID-19, accounting for spatial proximity. Among these models, MGWR exhibited superior performance. Key risk factors associated with the COVID-19 phenomenon identified, providing insights into the impact of household conditions, educational level of women, tobacco and alcohol consumption rates, number of health centres, and climatic factors. Moreover, the local coefficients estimated by MGWR model furnish detailed information regarding the strength and direction of the relationships between predictors and COVID-19 cases and deaths within each spatial unit. The findings emphasize the significance of addressing multicollinearity in spatial modelling. It is beneficial for accurate parameter estimation, proper interpretation of coefficients, improved spatial analysis, and providing reliable insights to support decision-making in spatial contexts.

The effects of trade liberalization on inequality in nutrition intake: empirical evidence from Indian districts

BackgroundDespite the positive impact of trade liberalization on food availability in India, severe inequality in nutrition consumption at the district level persists. Empirical evidence on the relationship between trade liberalization and nutrition consumption inequality often offers a country-level perspective and generates disputed outcomes. The study aimed to explore the effects of trade liberalization on inequality in nutrition consumption at the district level in India and to examine the heterogeneity of the impact on different nutrition consumption.MethodsOur study employed the Gini Index to measure nutrition consumption inequality of 2 macronutrients and 5 micronutrients at the district level in India during 2009–2011, utilizing the comprehensive FAO/WHO individual food consumption data. The import tariff was adopted as a proxy for trade liberalization, as its externally imposed nature facilitates a causal interpretation. We further identified the direct causal relationship between food trade liberalization and inequality in nutrition consumption using a fixed effects model.ResultsThe results show that more than 50% of the individuals in the survey districts did not meet the dietary standards for both macronutrients and micronutrients. Food trade liberalization hindered the improvement of inequality in nutrition consumption. As import tariffs were reduced by 1%, the inequality in intake of calories, zinc, vitamin B1, and vitamin B2 increased significantly by 0.45, 0.56, 0.48, and 0.66, respectively, which might be related to food market performance. The results also highlight the positive role of the gender gap, female-headed households, and caste culture on inequality in nutrition consumption in India.ConclusionsTo ease the shock of liberalization and minimize its inequality effects, complementary measures should be adopted, such as improving food logistic conditions in poor areas, and nutrition relief schemes.

High resolution prediction and explanation of groundwater depletion across India

Food production in much of the world relies on groundwater resources. In many regions, groundwater levels are declining due to a combination of anthropogenic extraction, localized meteorological and geological characteristics, and climate change. Groundwater in India is characteristic of this global trend, with an agricultural sector that is highly dependent on groundwater and increasingly threatened by extraction far in excess of recharge. The complexity of inputs makes groundwater depletion highly heterogeneous across space and time. However, modeling this heterogeneity has thus far proven difficult. Using two ensemble tree-based regression models, we predict district level seasonal groundwater dynamics to an accuracy of R 2 = 0.4–0.6 and Pearson correlations between 0.6 and 0.8. Further using two high-resolution feature importance methods, we demonstrate that atmospheric humidity, groundwater groundwater-based irrigation, and crop cultivation are the most important predictors of seasonal groundwater dynamics at the district level in India. We further demonstrate a shift in the predictors of groundwater depletion over 1998–2014 that is robustly found between the two feature importance methods, namely increasing importance of deep-well irrigation in Central and Eastern India. These areas coincide with districts where groundwater depletion is most severe. Further analysis shows decreases in crop yields per unit of irrigation over those regions, suggesting decreasing marginal returns for largely increasing quantities of groundwater irrigation used. This analysis demonstrates the public policy value of machine learning models for providing high spatiotemporal accuracy in predicting groundwater depletion, while also highlighting how anthropogenic activity impacts groundwater in India, with consequent implications for productivity and well-being.

The Contributions of the District Administration for Overall Development of its Local People in India: A Theoretical Study

The District Administration is the management of public affairs within a territory marked off for that purpose. Moreover, District Administration is that portion of public administration which functions within the territorial limits of a district. There are six approaches such as Fesler’s four approaches, the global approach and the Indian approach existing in the field of decentralization or district administration. The term “decentralization” implies not only the devolution of powers, but also a process in which responsibilities and duties are transferred by a higher or central authority to the institutions or organizations at the lower levels, thereby providing to the latter adequate incentive for autonomous functioning for the development of its particular local district people in India. There are many developmental benefits for the people because of the functioning of district administration in their respective local district level in India.

Assessing Child Health in India: Development and Validation of a Comprehensive Child Health Index

While developing a child health index is of interest, it is particularly important in the Indian context, because of a high burden of poor child health, the disparities in child health between different social, economic, and geographical regions and the absence of such a comprehensive index. This paper focuses on the development and validation of a Child Health Index (CHI) for India. Using publicly available data at the district level and principal component analysis, this index is composed of 16 variables representing six domains namely socio-cultural, child health status, determinants of child health (both risk and protective factors), household environment and health system and policy. Several statistical tests were conducted to provide internal and external validation. The application to predict child mortality confirmed its validation. This study thus provides a new tool for characterising child health and detecting child health inequalities at a district level in India. Consequently, it can be used by policymakers, health service providers and other stakeholders involved in child welfare to monitor and improve child health over time and space.

Does Public Spending Reflect the Need for Health: A Cross-sectional Analysis at District Level in India.

There is mixed evidence on the extent of association between the allocation of public revenue for healthcare and its indicators of need. In this study, we examined the relationship between allocations through state health financing (SHF) and the Central Government with infant mortality. District-wise infant mortality rate (IMR) was computed using National Family Health Survey-4 data. State-wise data for health budgets through SHF and National Health Mission (NHM, a Centrally Sponsored Scheme), were obtained for the year 2015-16. We used a multivariable analysis through generalized linear model method using identity-link function. We found per capita SHF (₹3169) to be more than 12 times that of public health spending per capita through NHM (₹261). IMR was lower in districts with higher SHF allocation, although statistically insignificant. The allocation through NHM was higher in districts with higher IMR, which is statistically significant. Every unit percentage increase in per capita net state domestic product and female literacy led to 0.31% and 0.54% decline, while a 1% increase in under-five diarrhoea prevalence led to 0.17% increase in IMR. The NHM has contributed to enhancing vertical equity in health-care financing. The States' need to be more responsive to the differences in districts while allocating health-care resources. There needs to be a focus on spending on social determinants, which should be the cornerstone for any universal health coverage strategy.

Unveiling the spatial pattern and determinants of child anaemia in India: National family health survey-5 chronicles (NFHS-5)

Background/Aim: Childhood anaemia continues to persist as a prominent nutritional disease and a public health challenge in India despite several initiatives by the Government of India. This study aimed to identify predictors and regional disparities for targeted interventions. Methods: This study utilised data from a nationally representative cross-sectional survey from the fifth round of the National Family Health Survey (NFHS-5), encompassing 177,695 children aged 6-59 months across 707 districts and 36 states and union territories of India. It employed multivariate logistic regression and spatial analysis at district levels to examine socio-demographic predictors and spatial patterns of childhood anaemia in the country. Result: Multivariate logistic results revealed, women aged 15-19 were 2.43 times more likely to have an anaemic child compared to those aged 35-49 and uneducated mothers had a 29 % higher likelihood of having an anaemic child. There was positive spatial autocorrelation (Moran's I value = 0.579) at the district level in India, with 108 identified hotspots in regions including Jammu and Kashmir, Ladakh, Gujarat, Maharashtra, Telangana, Uttar Pradesh, Rajasthan, Jharkhand, Chhattisgarh and Bihar. The spatial error model (SEM) indicated that mother's anaemia (0.53) and maternal education (0.23) were key predictors of child anaemia in India. Conclusion: The study findings provide valuable understanding regarding the socio-demographic predictors associated with childhood anaemia such as adolescent motherhood, low education, lack of media exposure, higher birth order and rural residence. Also, the spatial study provides the spatial heterogeneity of childhood anaemia at the district level and advocates more attention toward hotspot regions in the country.

Climate change vulnerability, and health of women and children: Evidence from India using district level data.

To understand how climate change vulnerability is associated with women and children's health (WCH) at the district level in India. The district-specific climate change vulnerability index was mapped to the district level NFHS-5 data (N= 674). Fractional regression and spatial analyses were performed to examine the strength of association and the presence of geographic clustering. Bivariate analysis revealed that the levels of WCH indicators were lower in districts with a high vulnerability index than in those with a low vulnerability index. Multivariable analyses suggested that with a 1% increase in the vulnerability index, the proportion of modern contraceptive use was reduced by 0.22, four or more prenatal care visits by 0.14, postnatal care by 0.11, and full immunization by 0.12; whereas wasting and underweight proportions increased by 0.07 and 0.10, respectively. The spatial analysis found that in about 70-118 districts, mostly in eastern India, where climate vulnerability was high the WCH outcomes were also poor. There is a macro-level association between climate change vulnerability and WCH, as districts that had high levels of climate change vulnerability also performed poorly in WCH. There is a need for an integrated approach that considers geography-specific climate change threats to develop health programs.

Mapping of space–time patterns of infectious disease using spatial statistical models: a case study of COVID-19 in India

Introduction Mapping of infectious diseases like COVID-19 is the foremost importance for diseases control and prevention. This study attempts to identify the spatio-temporal pattern and evolution trend of COVID-19 at the district level in India using spatial statistical models. Materials and methods Active cases of eleven time-stamps (30 March–2 December, 2020) with an approximately 20-day interval are considered. The study reveals applications of spatial statistical tools, i.e. optimised hotspot and outlier analysis (which follow Gi* and Moran I statistics) and emerging hotspot with the base of space time cube, are effective for the spatio-temporal evolution of disease clusters. Results The result shows the overall increasing trend of COVID-19 infection with a Mann–Kendall trend score of 2.95 (p = 0.0031). The spatial clusters of high infection (hotspots) and low infection (coldspots) change their location over time but are limited to the districts of the south-western states (Kerala, Karnataka, Andhra Pradesh, Maharashtra, Gujarat) and the north-eastern states (West Bengal, Jharkhand, Assam, Tripura, Manipur, etc.) respectively. Conclusions A total of eight types of patterns are identified, but the most concerning types are consecutive (7.24% of districts), intensifying (15.13% districts) and persistent (24.34% of districts) which will help health policy makers and the government to prioritize-based resource allocation and control measures.

Availability of public health facilities and utilization of maternal and child health services in districts of India

ObjectiveThis study aims to address the spatial variation in the availability of public health facilities and utilization of Maternal and Child Health (MCH) services at district level in India. MethodsTwo nationally representative secondary data sources such as the National Family Health Survey 2015-16 and Census of India, 2011 were used to identify districts, which are lagging behind in terms of the availability and utilization of MCH services. Mapping and spatial clustering analyses was performed to identify underperforming districts in India. Further, Ordinary least square and spatial autoregressive models were used to determine the predictor for utilization of MCH services at the district level. ResultsSignificantly, less availability of public health facilities and utilization of MCH services were observed in districts of Jharkhand, Bihar, Uttar Pradesh, Madhya Pradesh, Chhattisgarh, and North-Eastern states as compared to the national average. A shortage of 72,198 (21.9%) doctors and 165,791 (17.5%) paramedical staff found at the country level. After controlling for socio-economic factors, we observed that availability of sub-centre, dispensary, and population hospital bed ratio were significant positive predictors for Antenatal Care (ANC) service utilization whereas, shortage of paramedical staff was a negative predictor for full immunization at district level in India. ConclusionsAdequate availability of health facilities, and human resources are needed in districts, which are underperforming in terms of availability and utilization of MCH services to improve utilization of MCH services.

Infant mortality and death clustering at the district level in India: A Bayesian approach

This study assessed the clustering of and spatial variations in infant mortality between districts in selected states of India using a Bayesian geoadditive model. The study utilized 10 years of retrospective birth history of women from the fourth round of NFHS-4 (2015–16). Findings suggest, except Kerala, there was a significant amount of clustering of infant deaths in families in the selected Indian states. The maximum impact of clustering was observed in Assam, followed by Madhya Pradesh, Bihar, Uttarakhand, and Uttar Pradesh. The estimated residual spatial effect was statistically significant in all the states, with the maximum effect being in Assam and Chhattisgarh. The risk of infant death in Assam was higher in the north-eastern districts and lower in the southern districts of the state. Mother's age at child birth had a nonlinear effect on infant death in all the states, although significant effects were observed only in Bihar and Assam. In both of these states, mother's age at child birth had a “U-shape,” showing that the risk of infant death was higher at both earlier and later ages of mother's reproductive period. With the exception of Kerala, all the other selected states in the study had an “elongated L shaped” pattern, showing that in the early ages of the reproductive period, the risk of infant death was very high and that it gradually decreased with age and remained constant thereafter. In Kerala, mother's age at child birth was a straight line, implying that the risk of infant death was constant across the reproductive age of women. In order to keep infant mortality at a low level and to achieve better maternal and child health outcomes, the government needs to target families experiencing multiple infant deaths. In addition, programs must take into consideration the prevailing state-specific spatial heterogeneity in infant deaths and factors like mother's age at child birth.

Do Neighbourhood Effects Matter for the Geographical Concentration? Evidence from the Indian Industries

This paper investigates the impact of neighbouring effects on the geographical concentration of manufacturing and service industries at the district level using India’s Economic Census (2013) data. As empirical literature suggests, spillovers do not recognize areal boundaries, and spatial dependence among regions needs to be incorporated while quantifying the geographical concentration of industries. In this context, we employ the spatially weighted Ellison-Glaeser (EG) index to evaluate the impact of neighbourhood effect on the spatial concentration of 71 manufacturing and 120 service industries in India. Using aggregate data at the district level by covering 636 districts and 34 states and union territories in India, empirical results exhibit that the magnitude of the neighbourhood effect does not substantially impact the geographical concentration of 191 industries. More specifically, the neighbourhood effect is over-shadowed while considering an aggregate of 636 districts covering all states and union territories in India. To gain more insight into the role of neighbourhood effects and for robustness checks, we measure manufacturing and service industries’ geographical concentration within India’s 29 contiguous states and union territories. Our subsequent empirical evidence validates that neighbourhood effects are well captured by the spatially weighted Ellison-Glaeser index for the top three highly concentrated manufacturing and service industries. Moreover, we find that the spatially weighted EG index plays a predominant role while computing geographical concentration for the highly concentrated manufacturing and service industries across various Indian states and union territories at the district level in India.

COVID-19 second wave: District level study of concentration of confirmed cases and fatality in India

The paper aims to reveal the spatial pattern of the concentration of COVID-19 confirmed cases and the spread of the pandemic from the Case Fatality Ratio. The study has been accomplished with district-level data. The analysis of the spatial pattern decoding has been done considering the Global and Local Moran's I statistics comprising the linear trend of spatial autocorrelation for the whole India. The timeframe has been divided considering the surge of the second wave in March, 2021 and the peak of the wave in May 2021. The spatial clustering technique presents both the concentration of confirmed cases using Location Quotient analysis and the pattern of spread of the infection-related fatality throughout the country. The high Location Quotient of the confirmed cases strongly clustered around the Mumbai-Puna region, Kerala-Karnataka region, Garhwal Himachal, NCT of Delhi and Ladakh-Kashmir-Himachal Pradesh region during the period of the study. In May, the concentration has randomly clustered around the middle part of India. The Case Fatality Ratio was high in Maharashtra, Madhya Pradesh, Punjab and Haryana at the surge of the second wave. During the peak (May), two significant clusters of high Case Fatality Ratio are observed in and around the Mumbai urban (Maharashtra) and NCT of Delhi (including Punjab-Haryana).

Remote Sensing Based Yield Estimation of Rice (Oryza Sativa L.) Using Gradient Boosted Regression in India

Accurate and spatially explicit yield information is required to ensure farmers’ income and food security at local and national levels. Current approaches based on crop cutting experiments are expensive and usually too late for timely income stabilization measures like crop insurances. We, therefore, utilized a Gradient Boosted Regression (GBR), a machine learning technique, to estimate rice yields at ~500 m spatial resolution for rice-producing areas in India with potential application for near real-time estimates. We used resampled intermediate resolution (~5 km) images of the Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and observed yields at the district level in India for calibrating GBR models. These GBRs were then used to downscale district yields to 500 m resolution. Downscaled yields were re-aggregated for validation against out-of-sample district yields not used for model training and an additional independent data set of block-level (below district-level) yields. Our downscaled and re-aggregated yields agree well with reported district-level observations from 2003 to 2015 (r = 0.85 & MAE = 0.15 t/ha). The model performance improved further when estimating separate models for different rice cropping densities (up to r = 0.93). An additional out-of-sample validation for the years 2016 and 2017, proved successful with r = 0.84 and r = 0.77, respectively. Simulated yield accuracy was higher in water-limited, rainfed agricultural systems. We conclude that this downscaling approach of rice yield estimation using GBR is feasible across India and may complement current approaches for timely rice yield estimation required by insurance companies and government agencies.

Spatial analysis of COVID-19 incidence and its determinants using spatial modeling: A study on India

The first incident of COVID-19 case in India was recorded on 30th January, 2020 which turns to 100,000 marks on May 19th and by June 3rd it was over 200,000 active cases and 5,800 deaths. Geographic Information System (GIS) based spatial models can be helpful for better understanding of different factors that have triggered COVID-19 spread at district level in India. In the present study, 19 variables were considered that can explain the variability of the disease. Different spatial statistical techniques were used to describe the spatial distribution of COVID-19 and identify significant clusters. Spatial lag and error models (SLM and SEM) were employed to examine spatial dependency, geographical weighted regression (GWR) and multi-scale GWR (MGWR) were employed to examine at local level. The results show that the global models perform poorly in explaining the factors for COVID-19 incidences. MGWR shows the best-fit-model to explain the variables affecting COVID-19 (R2= 0.75) with lowest AICc value. Population density, urbanization and bank facility were found to be most susceptible for COVID-19 cases. These indicate the necessity of effective policies related to social distancing, low mobility. Mapping of different significant variables using MGWR can provide significant insights for policy makers for taking necessary actions.

Regional inequality in the Janani Suraksha Yojana coverage in India: a geo-spatial analysis

IntroductionAlthough India has made significant progress in institutional delivery after the implementation of the National Rural Health Mission under which the Janani Suraksha Yojana (JSY) is a sub-programme which played a vital role in the increase of institutional delivery in public facilities. Therefore, this paper aims to provide an understanding of the JSY coverage at the district level in India. Further, it tries to carve out the factors responsible for the regional disparity of JSY coverage at district levels.MethodsThe study used the National Family Health Survey data, which is a cross-sectional survey conducted in 2015–16, India. The sample size of this study was 148,145 women aged 15–49 years who gave last birth in the institution during 5 years preceding the survey. Bivariate and multivariate regression analysis was used to fulfill the study objectives. Additionally, Moran’s I statistics and bivariate Local Indicator for Spatial Association (LISA) maps were used to understand spatial dependence and clustering of JSY coverage. Ordinary least square, spatial lag and spatial error models were used to examine the correlates of JSY utilization.ResultsThe value of spatial-autocorrelation for JSY was 0.71 which depicts the high dependence of the JSY coverage over districts of India. The overall coverage of JSY in India is 36.4% and it highly varied across different regions, districts, and even socioeconomic groups. The spatial error model depicts that if in a district the women with no schooling status increase by 10% then the benefits of JSY get increased by 2.3%. Similarly, if in a district the women from poor wealth quintile, it increases by 10% the benefits of JSY also increased by 4.6%. However, the coverage of JSY made greater imperative to understand it due to its clustering among districts of specific states only.ConclusionIt is well reflected in the EAGs states in terms of spatial-inequality in service coverage. There is a need to universalize the JSY programme at a very individual level. And, it is required to revisit the policy strategy and the implementation plans at regional or district levels.