System and Neural Network Analysis of Economic and Financial Development – A case study of Dubai and rest of UAE

The study examines the factors affecting the economic and financial development by applying Zellner’s seemingly unrelated regressions (SURE) and Neural Network techniques. It applies multivariate and neural network frameworks for analysing the GDP of Dubai and rest of UAE using data for 2001–2015. The study shows that there exists positive interdependencies between Dubai and rest of UAE economies. This signifies that the core competencies across various sectors in Dubai and rest of UAE economies need to be promoted further to have overall diversified impact on UAE economy. The positive sizable impact of the finance sector in Dubai and negative sizable impact in the rest of the UAE provide many opportunities for designing diversification programs for sustained economic development of the entire UAE economy. The small sample size, non-availability of detailed sectoral data in four of the seven emirates constrained the scope of the study for generalization to other economies in the Middle East. The study findings are crucial for identifying structural reforms, to strengthen competitiveness and accelerate private sector-led job creation for nationals, potential on further opening up foreign direct investment (FDI), improving selected areas of the business environment, and easing access to finance for start-ups and SMEs in both the economies. JEL: C32, C52, D85, N15, N2

Sectoral Evaluation for Economic and Financial Development in Dubai and rest of UAE

The paper examines sector specific characteristics to analyse the factors affecting the sustainability of the economies of Dubai and rest of the United Arab Emirates (UAE). The study applies system design to analyse the research questions. Consequently, Zellner’s seemingly unrelated regressions (SURE) technique is used to examine the relative contribution of sectors to the economies Dubai, as an individual Emirate, and the rest of UAE as a group of Emirates using time series sectoral level data for 2001–2015. The study shows that there exists positive interdependencies between Dubai and rest of UAE economies. This signifies that the core competencies across various sectors in Dubai and rest of UAE economies need to be promoted further to have overall diversified impact on UAE economy. The positive sizable impact of the finance sector in Dubai and negative sizable impact in the rest of the UAE provide many opportunities for designing diversification programs for sustained economic development of the entire UAE economy. The small sample size, non-availability of detailed sectoral data in four of the seven emirates constrained the scope of the study for generalization to other economies in the middle east.   The study findings are very crucial for identifying structural reforms, to strengthen competitiveness and accelerate private sector-led job creation for nationals, potential on further opening up foreign direct investment (FDI), improving selected areas of the business environment, and easing access to finance for start-ups and SMEs in both the economies. There are very few studies, which have researched the sector specific characteristics to explain the factors affecting the sustainability of the economies of Dubai and the rest of UAE. The study provides insights to the UAE policy makers, for enhancement of policies through development of the key sectors that influence the performance of the two economies. Despite being independent entities though, the seven emirates of the UAE are economically interdependent. Studies on such interactions add unique value to the literature. Keywords: SURE, GDP, Dubai, UAE, Sectoral Evaluation, Financial development

Predicting Business Distress Using Neural Network in SME-Arab Region

The paper analyzes the financial and operational measures for Small and medium-sized enterprises (SME) business distress for predicting credit worthiness by using panel data of 110 observations from 22 SME companies for a period of 5 years (2009 – 2013). Panel logistic and Neural Network (NN) models are developed as alternative techniques for predicting the business distress.  The result suggests that cash cycle, net fixed assets, and leverage ratio are key factors in making credit decisions by lenders. The logistic model overall correctly classified 70 percent while NN framework outperformed the logistic model with 93 percent overall correct classification in training phase, and 83 percent in testing phase. The study opens up potential opportunities for the lending firms to adopt advanced analytical frameworks for predicting distress behavior of business firms. Keywords: SME, Business distress, Arab region, Petrochemical sub-sectors, Logit Model, Neural Network.   JEL codes: G29, G3

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions