Single Composite Index Research Articles

A review of ecosystem condition accounts: lessons learned and options for further development

Ecosystem condition accounts are part of the System of Environmental-Economic Accounting – Experimental Ecosystem Accounting (SEEA EEA). An ecosystem condition account contains aggregated statistical information about the overall abiotic and biotic quality of an ecosystem at a policy relevant spatial scale. This article reviews 23 publicly-accessible reports undertaken or commissioned by government agencies, academic and non-government organisations that discuss or present an ecosystem condition account. This analysis revealed that ecosystem condition is usually reported for one or more ecosystem types, but there is little consistency in the terminology used to define ecosystem types. All case studies report variables or indicators that measure specific ecosystem characteristics in order to make inferences about the overall condition of ecosystems. All studies included biotic indicators and almost all studies included species-based indicators in the condition account. The thematic aggregation of indicators into a single composite index (or in a few composite sub-indices) is not a standard practice, but applied in about half of the studies. The definition and use of a reference condition or reference levels for specific indicators against which the reported condition can be evaluated is not a standard practice, but was applied in about half of the studies. Based on this analysis, we suggest the revision of the SEEA EEA to propose a globally-consistent typology of ecosystem types; to recommend a list of ecosystem condition indicators according to an agreed classification; to provide further guidance on aggregation methods and on the development of an ecosystem condition index that can be used to compare ecosystem condition across ecosystem types and across different accounting areas; to provide further guidance on how best to set reference levels and reference conditions against which the past, current and future ecosystem condition can be assessed; and to propose a standard set of statistical tables for reporting the condition account.

Digital transformation, development and productivity in developing countries: is artificial intelligence a curse or a blessing?

PurposeThe entire world is now witnessing the Fourth Industrial Revolution and Artificial Intelligence (AI) is indeed altering the lives of the many in both developing and developed countries. Massive digital transformations are affecting the economies of those countries and are bringing with them many promised merits, as well as many challenges to face. This paper aims to examine the relationship between digital transformation (as a one facet of the fourth revolution and AI trends) on one side, and economic development, labor productivity and employment on the other side.Design/methodology/approachThe paper analyzes different indices of digital transformation, and then uses the Digital Evolution Index (DEI) to study those relationships in a group of developing countries using feasible generalized least squares method (FGLS).FindingsThe results show a positive relationship between the digital transformation index and economic development, labor productivity and job employment. Females seem to gain more from digital transformation compared to males, as suggested by the positive relation with the first and the insignificant relation with the latter. The relationship with vulnerable employment is not significant; more evidence is still needed to judge whether digital transformation will have an impact upon the vulnerable employees in the economy.Research limitations/implicationsThe paper focused on the impact of digital transformation upon total aggregate employment. Future research is still needed to examine the impact upon the structure of the labor market and the shift of occupations.Originality/valueThe paper aims to add to in the literature regarding the relationship between digital transformation, economic development, employment and productivity in the developing world. The implications of those relationships are of significant importance to policymakers regarding how much support should be given to encourage the digital transformation. At the same time, it shall also indicate how much social support policies are required – if any – to lessen the negative impact of digital transformation on the vulnerable groups inside the country. Another contribution is using a single composite index for digital transformation that is comparable across the chosen set of developing countries, instead of using single indices each capturing a different dimension of digital transformation.

Determinants of under-nutrition among children under five years of age in Ethiopia

BackgroundEthiopia is one of the developing countries where child under-nutrition is prevalent. Prior studies employed three anthropometric indicators for identifying factors of children’s under-nutrition. This study aimed at identifying the factors of child under-nutrition using a single composite index of anthropometric indicators.MethodsData from Ethiopia’s Demographic and Health Survey 2016 was the base for studying under-nutrition in a sample of 9494 children below 59 months. A single composite index of under-nutrition was created from three anthropometric indices through principal component analysis recoded into an ordinal outcome. In line with World Health Organization 2006 Child Growth Standards, the three anthropometric indices involve z-score of height-for-age (stunting), weight-for-height (wasting) and weight-for-age (underweight). Partial proportional odds model was fitted and its relative performance compared with some other ordinal regression models to identify significant determinants of under-nutrition.ResultsThe single composite index of anthropometric indicators showed that 49.0% (19.8% moderately and 29.2% severely) of sampled children were undernourished. In the Brant-test of proportional odds model, the null hypothesis that the model parameters equal across categories was rejected. Compared to ordinal regression models, partial proportional odds model showed an improved fit. A child with mother’s body mass index less than 18.5 kg, from poorest family and a husband without education, and male to be in a severe under-nutrition status was 1.4, 1.8 1.2 and 1.2 times more likely to be in worse under-nutrition status compared to its reference group respectively.ConclusionAuthors conclude that the fitted partial proportional odds model indicated that age and sex of the child, maternal education, region, source of drinking water, number of under five children, mother’s body mass index and wealth index, anemic status of child, multiple births, fever of child before 2 months of the survey, mother’s age at first birth, and husband’s education were significantly associated with child under-nutrition. Thus, it is argued that interventions focus on improving household wealth index, food security, educating mothers and their spouses, improving maternal nutritional status, and increasing mothers’ health care access.

Manufacturing enterprise performance using Network DEA: A profitability and marketability framework

Despite providing a holistic view of a company's progress, existing performance measurement system (PMS) models report performance separately for every single measure and leaves the overall performance of the corporate untouched. The concept of vertical and horizontal alignment has been described theoretically in PMS literature. However, the existing PMSs do not provide a mechanism for how to link each organisational level in a PMS. This study aims to promote the use of network data envelopment analysis (NDEA) for measuring manufacturing enterprises' performance. NDEA can deliver a single composite index, derived from multidimensional measures, which can represent the overall manufacturing performance. An alignment concept as a prerequisite of an integrated PMS is explained using the NDEA technical properties. The proposed NDEA-based PMS is tested in a pharmaceutical company to illustrate how it works in evaluating profitability and marketability. We modify an existing profitability and marketability framework into a three-stage model: production acquisition, profit earning, and marketability, to capture the complexity in manufacturing operations. The proposed NDEA-based PMS is useful for conducting a performance review to seek the sources of inefficiency and determine improvement target.

Constructing a comprehensive disaster resilience index: The case of Italy.

Measuring disaster resilience is a key component of successful disaster risk management and climate change adaptation. Quantitative, indicator-based assessments are typically applied to evaluate resilience by combining various indicators of performance into a single composite index. Building upon extensive research on social vulnerability and coping/adaptive capacity, we first develop an original, comprehensive disaster resilience index (CDRI) at municipal level across Italy, to support the implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030. As next, we perform extensive sensitivity and robustness analysis to assess how various methodological choices, especially the normalisation and aggregation methods applied, influence the ensuing rankings. The results show patterns of social vulnerability and resilience with sizeable variability across the northern and southern regions. We propose several statistical methods to allow decision makers to explore the territorial, social and economic disparities, and choose aggregation methods best suitable for the various policy purposes. These methods are based on linear and non-liner normalization approaches combining the OWA and LSP aggregators. Robust resilience rankings are determined by relative dominance across multiple methods. The dominance measures can be used as a decision-making benchmark for climate change adaptation and disaster risk management strategies and plans.

Stochastic Dominance Approach to OECD’s Better Life Index

Ever since the inception of OECD’s Better Life Index in 2011, a string of literature have emerged offering different aggregation procedures for the 11 dimensions of the index encompassing the broad categories of material living standards and quality of life. What is the most optimistic weighting scheme that allows more countries to achieve better measured well-being outcomes? What is the most pessimistic weighting scheme that worsens outcomes for more countries? Stochastic dominance efficiency is a data driven aggregation method that allows us to answer such questions which may be beneficial to policy makers and researchers. We offer rankings of countries across dimensions as well as rankings based on a single composite index aggregating all dimensional indicators. This type of analysis not only presents an opportunity to examine the sensitivity associated with re-weighting indicators, but this approach also reveals which indicators are driving overall improvement in measured well-being and which ones are hindering it. We find that the worst-case scenario rankings are generally more correlated with the equal-weighting scheme. And the best-case scenario weights offer a far more equal distribution of achievements across countries.

Delineation of flood-prone areas using geomorphological approach in the Mekong River Basin

For many river basins, flood maps have not been developed, especially in developing countries. Generally, flood hazard maps are generated by a modeling process based on past flood events and require a large amount of input data for the basin. The present study aims to delineate flood-prone areas in the Mekong River Basin from geomorphological features by using linear binary classifiers and receiver operating characteristics (ROC) analysis. Our method investigates the performance of five single features and six composite indices associated with flood hazards. The results indicate that elevation difference to the nearest river network has the best performance among all single features and composite indices with predicted abilities success rate SR = 63.15% and modified success rate MSR = 78.21%. This study shows that the combination of linear binary classifiers and ROC analysis can be used to detect flood-prone areas not only in small basins but also in a large basin such as the Mekong River Basin. This approach is advantageous for basins that lack observation data because the method does not require a large amount of basin information (i.e. all features and indices are derived from a digital elevation model). The outcomes of this study can provide useful information for identifying areas that are prone to flooding.

From Dependent to Independent Variable: A Critical Assessment of Operationalisations of ‘Welfare Stateness’ as Macro-Level Indicators in Multilevel Analyses

This paper explores different ways to operationalise properties of the welfare state as explanatory variable in multilevel frameworks. Based on the observation that many common applications of welfare state measurements as independent macro-level variables lack standardised proceeding, differences between commonly used approaches (single indicators, regime typologies, and composite indices) are examined concerning their consequences for empirical results and their comparability. Each approach is first discussed regarding conceptual premises and practical applications in the literature. In a second step, differences are demonstrated empirically by performing several multilevel analyses using welfare attitudes as an exemplary dependent variable. The comparison shows that even slight differences in the operationalisation have an impact on the results and their explanatory contribution. Based on this, the paper offers possible points of departure for the development of more fitting operationalisations for the specific use as explanatory instruments. This includes criteria a measurement should meet and a stronger focus on capturing the relevant causal mechanisms assumed to be at work. The aim of this paper is thus twofold. On the one hand, it provides an overview of existing strategies and raises awareness to critical issues. On the other hand, it gives some initial pointers for a conceptual debate about how to turn characteristics of the welfare state into macro-level indicators, which can serve as independent variable.

Abstract 034: Tracking Lifestyle Behaviors, Healthcare Access, and Healthcare Quality for Cardiometabolic Diseases at the State-Level From 1990-2016

Introduction: While a range of population-level and clinical interventions have been implemented to improve cardiometabolic (CM) health in the US, little is known about their different effects at the state-level. Objective: To develop a novel index to evaluate the performance of the healthcare system and population-level interventions to improve CM health at the state-level from 1990 to 2016. Methods: To evaluate healthcare access and quality, we estimated risk-standardized age-standardized mortality rates for six CM diseases that are amenable to healthcare. Risk-standardization removed geographic variation in all risk factors not directly amenable to medical intervention. To evaluate the effect of population-level interventions, we estimated the risk-weighted exposure to lifestyle risk factors including smoking, alcohol, diet, body mass index, and physical activity. We averaged the healthcare index with the risk factor index to create a single composite index. Data sources included mortality and risk factor estimates from the Global Burden of Disease 2016 Study. Results: Between 1990 and 2016, healthcare access and quality for CM diseases significantly improved in 38 states. These increases were mainly driven by significant improvements nationwide in healthcare for ischemic heart disease, ischemic stroke, and rheumatic heart disease. Notably, healthcare for diabetes significantly worsened in 16 states. There were no significant changes in the lifestyle risk factor index since 1990. Stability was driven by diverging trends, with smoking and diet quality significantly improving and BMI significantly worsening in all states. Importantly, the gap between the best and worst performing states across all indices increased between 1990 and 2016, indicating greater health disparities. Conclusions: This study has quantified the separate and combined effects of healthcare access, quality, and risk factors on CM health, with implications on priority setting for both population-level and clinical interventions.

Infant and young child feeding index and its association with nutritional status: A cross-sectional study of urban slums of Ahmedabad

BACKGROUND:Infant and young child feeding (IYCF) practices are multidimensional and change rapidly in short intervals in the 1st year of life, asking for simultaneous assessment of various feeding dimensions in children of 6 months and older. Infant and Child Feeding Index (ICFI) is a composite index which measures complete feeding practices for infants and young children. The present study was conducted to assess IYCF practices for children aged 6–36 months in terms of ICFI and some sociodemographic factors and find out the association of ICFI with nutritional status.MATERIALS AND METHODS:A cross-sectional study was conducted from July 2015 to October 2015 in Girdharnagar ward of Ahmedabad. Two hundred and ten mother–child pairs were selected by two-staged cluster sampling and were interviewed using a schedule adapted from Knowledge, Practices, and Coverage 2000+ model questionnaire. Appropriate anthropometric measurements were taken and nutritional indicators were calculated. Chi-square test, t- test, and regression analysis were applied wherever required. Epi info version 7.0 and MS Excel 2007 were used for statistical analysis.RESULTS:Nearly 65.2% of the children were stunted, 43.3% were underweight, and 11.9% were wasted. Only 38.3% of the children were initiated on breastfeeding within 1 h of birth. Only 19.1% of the children were breastfed for 2 years and beyond. Meal frequency was adequate in 64.3% and dietary diversity of >4 food groups was given to only 15.7% of the children. Significant higher proportions of children with low ICFI scores had illiterate mothers, were older, and belonged to lower socioeconomic strata. There was statistically significant association of ICFI with all the three nutritional status indicators. Higher proportion of children with lower ICFI scores had lower weight-for-height Z-scores, weight-for-age Z-scores, and height-for-age Z-scores.CONCLUSION:The present study revealed that ICFI can be used to measure IYCF practices in a single composite index, which in turn can reflect the nutritional status of the children.

Comprehensive drought characteristics analysis based on a nonlinear multivariate drought index

It is vital to identify drought events and to evaluate multivariate drought characteristics based on a composite drought index for better drought risk assessment and sustainable development of water resources. However, most composite drought indices are constructed by the linear combination, principal component analysis and entropy weight method assuming a linear relationship among different drought indices. In this study, the multidimensional copulas function was applied to construct a nonlinear multivariate drought index (NMDI) to solve the complicated and nonlinear relationship due to its dependence structure and flexibility. The NMDI was constructed by combining meteorological, hydrological, and agricultural variables (precipitation, runoff, and soil moisture) to better reflect the multivariate variables simultaneously. Based on the constructed NMDI and runs theory, drought events for a particular area regarding three drought characteristics: duration, peak, and severity were identified. Finally, multivariate drought risk was analyzed as a tool for providing reliable support in drought decision-making. The results indicate that: (1) multidimensional copulas can effectively solve the complicated and nonlinear relationship among multivariate variables; (2) compared with single and other composite drought indices, the NMDI is slightly more sensitive in capturing recorded drought events; and (3) drought risk shows a spatial variation; out of the five partitions studied, the Jing River Basin as well as the upstream and midstream of the Wei River Basin are characterized by a higher multivariate drought risk. In general, multidimensional copulas provides a reliable way to solve the nonlinear relationship when constructing a comprehensive drought index and evaluating multivariate drought characteristics.

The Multimodal Assessment of Adult Attachment Security: Developing the Biometric Attachment Test.

BackgroundAttachment theory has been proven essential for mental health, including psychopathology, development, and interpersonal relationships. Validated psychometric instruments to measure attachment abound but suffer from shortcomings common to traditional psychometrics. Recent developments in multimodal fusion and machine learning pave the way for new automated and objective psychometric instruments for adult attachment that combine psychophysiological, linguistic, and behavioral analyses in the assessment of the construct.ObjectiveThe aim of this study was to present a new exposure-based, automatic, and objective adult-attachment assessment, the Biometric Attachment Test (BAT), which exposes participants to a short standardized set of visual and music stimuli, whereas their immediate reactions and verbal responses, captured by several computer sense modalities, are automatically analyzed for scoring and classification. We also aimed to empirically validate two of its assumptions: its capacity to measure attachment security and the viability of using themes as placeholders for rotating stimuli. MethodsA total of 59 French participants from the general population were assessed using the Adult Attachment Questionnaire (AAQ), the Adult Attachment Projective Picture System (AAP), and the Attachment Multiple Model Interview (AMMI) as ground truth for attachment security. They were then exposed to three different BAT stimuli sets, whereas their faces, voices, heart rate (HR), and electrodermal activity (EDA) were recorded. Psychophysiological features, such as skin-conductance response (SCR) and Bayevsky stress index; behavioral features, such as gaze and facial expressions; as well as linguistic and paralinguistic features, were automatically extracted. An exploratory analysis was conducted using correlation matrices to uncover the features that are most associated with attachment security. A confirmatory analysis was conducted by creating a single composite effects index and by testing it for correlations with attachment security. The stability of the theory-consistent features across three different stimuli sets was explored using repeated measures analysis of variances (ANOVAs).ResultsIn total, 46 theory-consistent correlations were found during the exploration (out of 65 total significant correlations). For example, attachment security as measured by the AAP was correlated with positive facial expressions (r=.36, P=.01). AMMI’s security with the father was inversely correlated with the low frequency (LF) of HRV (r=−.87, P=.03). Attachment security to partners as measured by the AAQ was inversely correlated with anger facial expression (r=−.43, P=.001). The confirmatory analysis showed that the composite effects index was significantly correlated to security in the AAP (r=.26, P=.05) and the AAQ (r=.30, P=.04) but not in the AMMI. Repeated measures ANOVAs conducted individually on each of the theory-consistent features revealed that only 7 of the 46 (15%) features had significantly different values among responses to three different stimuli sets.ConclusionsWe were able to validate two of the instrument’s core assumptions: its capacity to measure attachment security and the viability of using themes as placeholders for rotating stimuli. Future validation of other of its dimensions, as well as the ongoing development of its scoring and classification algorithms is discussed.

What are we measuring? An empirical analysis of household electricity access metrics in rural Bangladesh

Measuring energy access through binary indicators is insufficient, and in some cases even misleading. In this work, we critically evaluate the World Bank’s multi-tier framework (MTF) to measure household electricity access using a household survey in rural Bangladesh. We argue that the MTF addresses multiple objectives, thereby offering less value as a single composite index than as a set of dimensions along which to evaluate different aspects of electricity access. We test the robustness of the framework to alternative specifications as regards the choice of attributes and tier thresholds. The study shows that access measurement is highly sensitive to changes in parameter values, the application of different algorithms, and data availability. We also discuss the wider implications of applying the framework to current electricity access intervention programs in Bangladesh, provide feedback on the MTF’s design, as well as suggest potential improvements for its application in the future.

Higher breastfeeding performance index is associated with lower risk of illness in infants under six months in Ethiopia.

BackgroundBreastfeeding performance index is an explanatory attempt to summarize key breastfeeding practices by summarizing the different dimensions of breastfeeding practices into a single summary variable. Breastfeeding performance index is used to assess optimal breastfeeding practices by constructing a single composite index that includes timely initiation of breastfeeding, prelacteal feeding, current breastfeeding status, bottle feeding, any liquid given(except medicine) in the last 24 h, formula given in the last 24 h, any solid food given in the last 24 h. This study aimed to assess optimal breastfeeding practices of 0–6 month infants using breastfeeding performance index (BPI) and its association with childhood illness in Ethiopia.MethodsA secondary data analysis was carried out based on the Ethiopia Demographic and Health Survey (EDHS) 2011 data. The BPI was created using seven components by giving equal weight for all components during scoring. The data were described using descriptive statistics and analyzed using multivariable logistic regression.ResultsThe prevalence of low, medium, and high BPI was 18.41, 57.96 and 23.63 % respectively. The mean BPI score was 4.38 (SD 1.25; 95 % CI 4.31, 4.45). Multivariable logistic regression analysis showed that those infants who had low BPI score were 2.22 times (AOR = 2.22; 95 % CI 1.20, 4.11) and medium BPI category had 2.15 times at higher odds (AOR = 2.15; 95 % CI 1.23, 3.75) of developing diarrhea compared to infants in the highest BPI category. Being in the lower BPI category was significantly associated with higher odds of having fever (AOR = 1.73; 95 % CI 1.06, 2.80). Being in the medium index category was also associated with higher odds of having short and rapid breaths (AOR = 2.02; 95 % CI 1.01, 4.04).ConclusionMore than 80 % of the infants did not receive optimal breastfeeding practices based on the Breastfeeding Performance Index. Lower BPI was statistically associated with diarrhea, fever and short and rapid breaths illness in the last 2 weeks. This study implicates the importance of optimal breastfeeding to reduce childhood illness.

Assessment of composite index methods for agricultural vulnerability to climate change

A common way of quantifying and communicating climate vulnerability is to calculate composite indices from indicators, visualizing these as maps. Inherent methodological uncertainties in vulnerability assessments, however, require greater attention. This study examines Swedish agricultural vulnerability to climate change, the aim being to review various indicator approaches for assessing agricultural vulnerability to climate change and to evaluate differences in climate vulnerability depending on the weighting and summarizing methods. The reviewed methods are evaluated by being tested at the municipal level. Three weighting and summarizing methods, representative of climate vulnerability indices in general, are analysed. The results indicate that 34 of 36 method combinations differ significantly from each other. We argue that representing agricultural vulnerability in a single composite index might be insufficient to guide climate adaptation. We emphasize the need for further research into how to measure and visualize agricultural vulnerability and into how to communicate uncertainties in both data and methods.

Using financial indicators to predict turning points in the business cycle: The case of the leading economic index for the United States

In this paper, we evaluate the usefulness of financial indicators according to their ability to predict recessions (i.e., peaks in the business cycle). We then select a small set of financial indicators to aggregate into a single composite index of financial indicators, which we name the Leading Credit Index (LCI). Our approach differs from others in the literature in that we follow the composite index approach of the Leading Economic Index (LEI) of the United States and focus on a small, carefully selected set of indicators as index components, and, in addition, our selection criteria target business cycle turning points rather than financial stress or instability. We show that this leading credit index, either alone or as a component of the LEI, can be helpful in estimating recession probabilities, which it does better than the individual indicators, including some of the existing components of the LEI, especially real money supply.

Anosognosia in People with Cognitive Impairment: Association with Cognitive Deficits and Behavioral Disturbances

Aims: To investigate, in a group of subjects at an early stage of cognitive impairment, the relationship between anosognosia and both cognitive and behavioral symptoms by exploring the various domains of insight. Methods: One hundred and eight subjects affected by cognitive impairment were consecutively enrolled. The level of awareness was evaluated by means of the Clinical Insight Rating Scale (CIRS). Psychiatric symptoms were evaluated using the Italian version of the Neuropsychiatric Inventory (NPI), whereas memory (memory index, MI) and executive (executive index, EI) functions were explored using a battery of neuropsychological tests and qualified by means of a single composite cognitive index score for each function. Results: A significant positive correlation between the total NPI score and global anosognosia score was found. Furthermore, both the MI and EI scores were lower in subjects with anosognosia than in those without anosognosia (p < 0.001 and p < 0.007, respectively). When the single domains of the CIRS were considered, anosognosia of reason of visit correlated with the EI score (r = -0.327, p = 0.01) and night-time behavioral disturbances (r = 0.225; p = 0.021); anosognosia of cognitive deficit correlated with depression (r = -0.193; p = 0.049) and the MI score (r = -0.201; p = 0.040); anosognosia of functional deficit correlated with the MI score (r = -0.257; p = 0.008), delusions (r = 0.232; p = 0.015) and aberrant motor behavior (r = 0.289; p = 0.003); anosognosia of disease progression correlated with the MI score (r = -0.236; p = 0.015), agitation (r = 0.247; p = 0.011), aberrant motor behavior (r = 0.351; p = 0.001) and night-time behavioral disturbances (r = 0.216; p = 0.027). Conclusions: Our study suggests that, in the early stage of cognitive impairment, anosognosia is associated with both cognitive deficits and behavioral disorders according to the specific functional anatomy of the symptoms.

Real-time dynamic security assessment of power grids

This paper presents a dynamic security assessment solution, which can be used in the power system control room to improve system stability. It is based on a set of security indices. The indices are able of establishing contingencies’ severity levels as a measure of different aspects of power system security. A system based on fuzzy logic is used to combine the indices into a single composite index. The composite index is able to alert the control operator to the network conditions that represent a significant risk to system security based on over-all system performance.

New Directions in Globalization Indices

This paper discusses the measurement of globalization with a view to advancing the construction of globalization indices. It critically analyzes the types of indices that can contribute to knowledge and policy on globalization. Three issues are particularly highlighted: (a) the focus of measurement (i.e. on activities or policies); (b) the dimensions of measurement (i.e. cultural, ecological, economic, political, and/or social); and (c) the units of measurement (i.e. local, national, regional, and/or global). This paper argues that a workable forward strategy should not seek to identify the single best composite globalization index, but rather should work in an interdisciplinary mode towards a set of complementary globalization indices. These quantitative analyses can then be productively blended with qualitative approaches in a fuller assessment of globalization's extent and impact.

Macroeconomic Risks and Financial Stability: An Econometric Analysis for India*

While it is difficult to foresee the triggers for crisis, continuous monitoring and assessment of the buildup of risks need to be an integral part of any policy framework for maintaining overall economic and financial stability. This paper is an attempt in that direction. This paper initially attempts to identify the macro risk indicators, which has potential of causing instability to the economic system and which is then transmitted to the financial system. These risk indicators are then combined into single composite index as an indicator of overall macroeconomic stability. The various risk indicators have been appropriately validated through their impact on GDP and NPAs in the banking system. The paper also initially attempts to identify various financial market indicators which reveal the broad dynamics of the financial system. Similar to the macro risk indicators, these financial market indicators are combined into one composite index and then its relationship with macro risk indicators and their composite index is examined in the VAR framework.A VAR analysis reveals that both the composite indices of macro risks and market risks as developed in this paper have adverse impact on the growth in GDP. Among the various macro risk factors, global and external sector indicators appear to have more severe impact on the growth. The adverse impact of macro risk on growth of GDP was found to be immediate but the impact of market risks indicators increase in size and dominate over time. It is observed that the macro risk indicators have substantial adverse impact on the financial market indicator and its optimal lag works out to be about six months. Results of this exercise though exploratory in nature but are meaningful from policy perspective. One of the advantages of this analysis is its flexibility. It can easily be incorporated in a more comprehensive framework for assessment of systemic risks and for stress tests. The findings of this paper can be further developed while retaining the underlying basic structure.