Determinants of Gross Domestic Saving and its Trend Analysis in Turkey: A Time Series Outlook

Saving is an engine for the growth and prosperity of a nation by creating capital accumulation and financial investments through resource mobilization. Turkey appears in the set of countries that have relatively low domestic saving rates and relatively high current account deficits. This study examined the determinants of Gross Domestic Saving and its trend in Turkey, using time series data (annual) ranging from 1980-2018. Data were collected from the World Development Indicators (WDI) 2019 database, World Bank, and Central Bank of Turkey annual reports. The macroeconomic variables used in the model were Gross Domestic Saving rate to GDP ratio (GDS), Inflation Rate (INF), Deposit Interest Rate (DIR), Broad Money Supply to GDP ratio (M2R), Age-Dependency Ratio (ADR), and Growth of Gross National Income Per Capita (GNIPCG). The study has used the Auto-Regressive Distributed Lag (ARDL) model and appropriate diagnostic tests for model specification. The results of the study have shown that the first lag of Gross Domestic Saving, Inflation Rate, Age-Dependency Ratio, and Broad Money Supply to GDP ratio have positive effects, whereas Gross National Income Per Capita Growth and Deposit Interest Rate have negative effects on Gross Domestic Saving rate in Turkey. Only the first Lag of Gross Domestic Saving rate and Deposit Interest rate have statistically significant effects on Gross Domestic Saving in Turkey at a 5 percent level of significance. The rest of all variables have statistically insignificant effects. The overall findings of the study underlined the importance of adopting strict fiscal and monetary policies to regulate inflation and money supply with manageable levels to improve the Gross Domestic Saving rate in Turkey.

Analysis of technical efficiency of rice production in fogera district of Ethiopia: A stochastic frontier approach

The possible way to improve production and productivity with a given input mix and available technology is to improve efficiency of resource use. For this purpose examining the technical efficiency of the production process is very crucial. Thus, the aim of this paper is to analyze the technical efficiency of rice production in Fogera District of Ethiopia. To do so, stochastic frontier approach is employed on a data which is collected from 200 sample households in 2015/16 production year. The sampling techniques used to get those 200 sample households is a multistage sampling where in the first stage five Kebeles were purposively selected, in the second stage two Gotes randomly selected from each Kebeles and in the third stage 200 households were selected using simple random sampling technique. Doing so, it was found that except manure all the variables in the Cobb-Douglass stochastic frontier model which includes; land, fertilizer, oxen, seed and labor are found to be positively and significantly related to rice production. The average technical efficiency score predicted from the estimated Cobb-Douglas stochastic frontier production function is found to be 77.2% implying that there is a room for rice yield increment by improving the resource use efficiency of the households. The study also revealed that; provision of extension service, training on rice product improvement, experience on rice farming; agrochemical and education tend to be positively and significantly related to technical efficiency while household size is negatively and significantly related. Thus, strengthening extension service provision and training on rice yield increment, campaigns to disseminate rice farming experiences and increasing the supply of agrochemicals are crucial to improve the technical efficiency of rice production in the study area

Analysis of the technical efficiency of rice production in Fogera district of Ethiopia: a stochastic frontier approach

The aim of this paper is to analyze the technical efficiency of rice production in Fogera District of Ethiopia. To do so, the stochastic frontier approach was employed on a data collected from 200 sample households in the 2015/16 production year. As a result, it was found out that except for manure, all variables in the Cobb-Douglass stochastic frontier model, which includes land, fertilizer, oxen, seed, and labor, were positively and significantly related to rice production. The average technical efficiency score predicted from the estimated Cobb-Douglas stochastic frontier production function was calculated to be 77.2 percent, implying that there was a room for rice yield increment by improving the resource use efficiency of households. The study also revealed that the provision of extension services, training on rice product improvement, experience on rice farming, agrochemicals, and education tend to be positively and significantly related to technical efficiency while household size was negatively and significantly related. Thus, strengthening the extension service provision and the training on rice yield increment, campaigns to disseminate rice farming experiences, and an increase in the supply of agrochemicals were crucial to improve the technical efficiency of rice production in the study area.Keywords: Ethiopia, Fogera District, Technical Efficiency, Cobb-Douglas Production Function, Stochastic Frontier ApproachJEL classification: D2

Analysis of technical efficiency of rice production in fogera district of Ethiopia: A stochastic frontier approach

The possible way to improve production and productivity with a given input mix and available technology is to improve efficiency of resource use. For this purpose examining the technical efficiency of the production process is very crucial. Thus, the aim of this paper is to analyze the technical efficiency of rice production in Fogera District of Ethiopia. To do so, stochastic frontier approach is employed on a data which is collected from 200 sample households in 2015/16 production year. The sampling techniques used to get those 200 sample households is a multistage sampling where in the first stage five Kebeles were purposively selected, in the second stage two Gotes randomly selected from each Kebeles and in the third stage 200 households were selected using simple random sampling technique. Doing so, it was found that except manure all the variables in the Cobb-Douglass stochastic frontier model which includes; land, fertilizer, oxen, seed and labor are found to be positively and significantly related to rice production. The average technical efficiency score predicted from the estimated Cobb-Douglas stochastic frontier production function is found to be 77.2% implying that there is a room for rice yield increment by improving the resource use efficiency of the households. The study also revealed that; provision of extension service, training on rice product improvement, experience on rice farming; agrochemical and education tend to be positively and significantly related to technical efficiency while household size is negatively and significantly related. Thus, strengthening extension service provision and training on rice yield increment, campaigns to disseminate rice farming experiences and increasing the supply of agrochemicals are crucial to improve the technical efficiency of rice production in the study area

Analysis of technical efficiency of rice production in fogera district of Ethiopia: A stochastic frontier approach

The possible way to improve production and productivity with a given input mix and available technology is to improve efficiency of resource use. For this purpose examining the technical efficiency of the production process is very crucial. Thus, the aim of this paper is to analyze the technical efficiency of rice production in Fogera District of Ethiopia. To do so, stochastic frontier approach is employed on a data which is collected from 200 sample households in 2015/16 production year. The sampling techniques used to get those 200 sample households is a multistage sampling where in the first stage five Kebeles were purposively selected, in the second stage two Gotes randomly selected from each Kebeles and in the third stage 200 households were selected using simple random sampling technique. Doing so, it was found that except manure all the variables in the Cobb-Douglass stochastic frontier model which includes; land, fertilizer, oxen, seed and labor are found to be positively and significantly related to rice production. The average technical efficiency score predicted from the estimated Cobb-Douglas stochastic frontier production function is found to be 77.2% implying that there is a room for rice yield increment by improving the resource use efficiency of the households. The study also revealed that; provision of extension service, training on rice product improvement, experience on rice farming; agrochemical and education tend to be positively and significantly related to technical efficiency while household size is negatively and significantly related. Thus, strengthening extension service provision and training on rice yield increment, campaigns to disseminate rice farming experiences and increasing the supply of agrochemicals are crucial to improve the technical efficiency of rice production in the study area

National disability-adjusted life years(DALYs) for 257 diseases and injuries in Ethiopia, 1990–2015: findings from the global burden of disease study 2015

Background: Disability-adjusted life years (DALYs) provide a summary measure of health and can be a critical input to guide health systems, investments, and priority-setting in Ethiopia. We aimed to determine the leading causes of premature mortality and disability using DALYs and describe the relative burden of disease and injuries in Ethiopia. Methods: We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) for non-fatal disease burden, cause-specific mortality, and all-cause mortality to derive age-standardized DALYs by sex for Ethiopia for each year. We calculated DALYs by summing years of life lost due to premature mortality (YLLs) and years lived with disability (YLDs) for each age group and sex. Causes of death by age, sex, and year were measured mainly using Causes of Death Ensemble modeling. To estimate YLDs, a Bayesian meta-regression method was used. We reported DALY rates per 100,000 for communicable, maternal, neonatal, and nutritional (CMNN) disorders, non-communicable diseases, and injuries, with 95% uncertainty intervals (UI) for Ethiopia. Results: Non-communicable diseases caused 23,118.1 (95% UI, 17,124.4–30,579.6), CMNN disorders resulted in 20,200.7 (95% UI, 16,532.2–24,917.9), and injuries caused 3781 (95% UI, 2642.9–5500.6) age-standardized DALYs per 100,000 in Ethiopia in 2015. Lower respiratory infections, diarrheal diseases, and tuberculosis were the top three leading causes of DALYs in 2015, accounting for 2998 (95% UI, 2173.7–4029), 2592.5 (95% UI, 1850.7–3495.1), and 2562.9 (95% UI, 1466.1–4220.7) DALYs per 100,000, respectively. Ischemic heart disease and cerebrovascular disease were the fourth and fifth leading causes of age-standardized DALYs, with rates of 2535.7 (95% UI, 1603.7–3843.2) and 2159.9 (95% UI, 1369.7–3216.3) per 100,000, respectively. The following causes showed a reduction of 60% or more over the last 25 years: lower respiratory infections, diarrheal diseases, tuberculosis, neonatal encephalopathy, preterm birth complications, meningitis, malaria, protein-energy malnutrition, iron-deficiency anemia, measles, war and legal intervention, and maternal hemorrhage

National mortality burden due to communicable, non-communicable, and other diseases in Ethiopia, 1990–2015: findings from the Global Burden of Disease Study 2015

Background: Ethiopia lacks a complete vital registration system that would assist in measuring disease burden and risk factors. We used the Global Burden of Diseases, Injuries, and Risk factors 2015 (GBD 2015) estimates to describe the mortality burden from communicable, non-communicable, and other diseases in Ethiopia over the last 25 years. Methods: GBD 2015 mainly used cause of death ensemble modeling to measure causes of death by age, sex, and year for 195 countries. We report numbers of deaths and rates of years of life lost (YLL) for communicable, maternal, neonatal, and nutritional (CMNN) disorders, non-communicable diseases (NCDs), and injuries with 95% uncertainty intervals (UI) for Ethiopia from 1990 to 2015. Results: CMNN causes of death have declined by 65% in the last two-and-a-half decades. Injury-related causes of death have also decreased by 70%. Deaths due to NCDs declined by 37% during the same period. Ethiopia showed a faster decline in the burden of four out of the five leading causes of age-standardized premature mortality rates when compared to the overall sub-Saharan African region and the Eastern sub-Saharan African region: lower respiratory infections, tuberculosis, HIV/AIDS, and diarrheal diseases; however, the same could not be said for ischemic heart disease and other NCDs. Non-communicable diseases, together, were the leading causes of age-standardized mortality rates, whereas CMNN diseases were leading causes of premature mortality in 2015. Although lower respiratory infections, tuberculosis, and diarrheal disease were the leading causes of age-standardized death rates, they showed major declines from 1990 to 2015. Neonatal encephalopathy, iron-deficiency anemia, protein-energy malnutrition, and preterm birth complications also showed more than a 50% reduction in burden. HIV/AIDS-related deaths have also decreased by 70% since 2005. Ischemic heart disease, hemorrhagic stroke, and ischemic stroke were among the top causes of premature mortality and age-standardized death rates in Ethiopia in 2015. Conclusions: Ethiopia has been successful in reducing deaths related to communicable, maternal, neonatal, and nutritional deficiency diseases and injuries by 65%, despite unacceptably high maternal and neonatal mortality rates. However, the country’s performance regarding non-communicable diseases, including cardiovascular disease, diabetes, cancer, and chronic respiratory disease, was minimal, causing these diseases to join the leading causes of premature mortality and death rates in 2015. While the country is progressing toward universal health coverage, prevention and control strategies in Ethiopia should consider the double burden of common infectious diseases and non-communicable diseases: lower respiratory infections, diarrhea, tuberculosis, HIV/AIDS, cardiovascular disease, cancer, and diabetes. Prevention and control strategies should also pay special attention to the leading causes of premature mortality and death rates caused by non-communicable diseases: cardiovascular disease, cancer, and diabetes. Measuring further progress requires a data revolution in generating, managing, analyzing, and using data for decision-making and the creation of a full vital registration system in the country

Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: A systematic analysis from the Global Burden of Disease Study 2016

Copyright © 2018 The Author(s). Published by Elsevier Ltd. Background A key component of achieving universal health coverage is ensuring that all populations have access to quality health care. Examining where gains have occurred or progress has faltered across and within countries is crucial to guiding decisions and strategies for future improvement. We used the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) to assess personal health-care access and quality with the Healthcare Access and Quality (HAQ) Index for 195 countries and territories, as well as subnational locations in seven countries, from 1990 to 2016. Methods Drawing from established methods and updated estimates from GBD 2016, we used 32 causes from which death should not occur in the presence of effective care to approximate personal health-care access and quality by location and over time. To better isolate potential effects of personal health-care access and quality from underlying risk factor patterns, we risk-standardised cause-specific deaths due to non-cancers by location-year, replacing the local joint exposure of environmental and behavioural risks with the global level of exposure. Supported by the expansion of cancer registry data in GBD 2016, we used mortality-to-incidence ratios for cancers instead of risk-standardised death rates to provide a stronger signal of the effects of personal health care and access on cancer survival. We transformed each cause to a scale of 0-100, with 0 as the first percentile (worst) observed between 1990 and 2016, and 100 as the 99th percentile (best); we set these thresholds at the country level, and then applied them to subnational locations. We applied a principal components analysis to construct the HAQ Index using all scaled cause values, providing an overall score of 0-100 of personal health-care access and quality by location over time. We then compared HAQ Index levels and trends by quintiles on the Socio-demographic Index (SDI), a summary measure of overall development. As derived from the broader GBD study and other data sources, we examined relationships between national HAQ Index scores and potential correlates of performance, such as total health spending per capita. Findings In 2016, HAQ Index performance spanned from a high of 97·1 (95% UI 95·8-98·1) in Iceland, followed by 96·6 (94·9-97·9) in Norway and 96·1 (94·5-97·3) in the Netherlands, to values as low as 18·6 (13·1-24·4) in the Central African Republic, 19·0 (14·3-23·7) in Somalia, and 23·4 (20·2-26·8) in Guinea-Bissau. The pace of progress achieved between 1990 and 2016 varied, with markedly faster improvements occurring between 2000 and 2016 for many countries in sub-Saharan Africa and southeast Asia, whereas several countries in Latin America and elsewhere saw progress stagnate after experiencing considerable advances in the HAQ Index between 1990 and 2000. Striking subnational disparities emerged in personal health-care access and quality, with China and India having particularly large gaps between locations with the highest and lowest scores in 2016. In China, performance ranged from 91·5 (89·1-93·6) in Beijing to 48·0 (43·4-53·2) in Tibet (a 43·5-point difference), while India saw a 30·8-point disparity, from 64·8 (59·6-68·8) in Goa to 34·0 (30·3-38·1) in Assam. Japan recorded the smallest range in subnational HAQ performance in 2016 (a 4·8-point difference), whereas differences between subnational locations with the highest and lowest HAQ Index values were more than two times as high for the USA and three times as high for England. State-level gaps in the HAQ Index in Mexico somewhat narrowed from 1990 to 2016 (from a 20·9-point to 17·0-point difference), whereas in Brazil, disparities slightly increased across states during this time (a 17·2-point to 20·4-point difference). Performance on the HAQ Index showed strong linkages to overall development, with high and high-middle SDI countries generally having higher scores and faster gains for non-communicable diseases. Nonetheless, countries across the development spectrum saw substantial gains in some key health service areas from 2000 to 2016, most notably vaccine-preventable diseases. Overall, national performance on the HAQ Index was positively associated with higher levels of total health spending per capita, as well as health systems inputs, but these relationships were quite heterogeneous, particularly among low-to-middle SDI countries. Interpretation GBD 2016 provides a more detailed understanding of past success and current challenges in improving personal health-care access and quality worldwide. Despite substantial gains since 2000, many low-SDI and middle- SDI countries face considerable challenges unless heightened policy action and investments focus on advancing access to and quality of health care across key health services, especially non-communicable diseases. Stagnating or minimal improvements experienced by several low-middle to high-middle SDI countries could reflect the complexities of re-orienting both primary and secondary health-care services beyond the more limited foci of the Millennium Development Goals. Alongside initiatives to strengthen public health programmes, the pursuit of universal health coverage hinges upon improving both access and quality worldwide, and thus requires adopting a more comprehensive view - and subsequent provision - of quality health care for all populations

Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: A systematic analysis from the Global Burden of Disease Study 2016

Background: A key component of achieving universal health coverage is ensuring that all populations have access to quality health care. Examining where gains have occurred or progress has faltered across and within countries is crucial to guiding decisions and strategies for future improvement. We used the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) to assess personal health-care access and quality with the Healthcare Access and Quality (HAQ) Index for 195 countries and territories, as well as subnational locations in seven countries, from 1990 to 2016. Methods Drawing from established methods and updated estimates from GBD 2016, we used 32 causes from which death should not occur in the presence of effective care to approximate personal health-care access and quality by location and over time. To better isolate potential effects of personal health-care access and quality from underlying risk factor patterns, we risk-standardised cause-specific deaths due to non-cancers by location-year, replacing the local joint exposure of environmental and behavioural risks with the global level of exposure. Supported by the expansion of cancer registry data in GBD 2016, we used mortality-to-incidence ratios for cancers instead of risk-standardised death rates to provide a stronger signal of the effects of personal health care and access on cancer survival. We transformed each cause to a scale of 0-100, with 0 as the first percentile (worst) observed between 1990 and 2016, and 100 as the 99th percentile (best); we set these thresholds at the country level, and then applied them to subnational locations. We applied a principal components analysis to construct the HAQ Index using all scaled cause values, providing an overall score of 0-100 of personal health-care access and quality by location over time. We then compared HAQ Index levels and trends by quintiles on the Socio-demographic Index (SDI), a summary measure of overall development. As derived from the broader GBD study and other data sources, we examined relationships between national HAQ Index scores and potential correlates of performance, such as total health spending per capita. Findings In 2016, HAQ Index performance spanned from a high of 97\ub71 (95% UI 95\ub78-98\ub71) in Iceland, followed by 96\ub76 (94\ub79-97\ub79) in Norway and 96\ub71 (94\ub75-97\ub73) in the Netherlands, to values as low as 18\ub76 (13\ub71-24\ub74) in the Central African Republic, 19\ub70 (14\ub73-23\ub77) in Somalia, and 23\ub74 (20\ub72-26\ub78) in Guinea-Bissau. The pace of progress achieved between 1990 and 2016 varied, with markedly faster improvements occurring between 2000 and 2016 for many countries in sub-Saharan Africa and southeast Asia, whereas several countries in Latin America and elsewhere saw progress stagnate after experiencing considerable advances in the HAQ Index between 1990 and 2000. Striking subnational disparities emerged in personal health-care access and quality, with China and India having particularly large gaps between locations with the highest and lowest scores in 2016. In China, performance ranged from 91\ub75 (89\ub71-93\ub76) in Beijing to 48\ub70 (43\ub74-53\ub72) in Tibet (a 43\ub75-point difference), while India saw a 30\ub78-point disparity, from 64\ub78 (59\ub76-68\ub78) in Goa to 34\ub70 (30\ub73-38\ub71) in Assam. Japan recorded the smallest range in subnational HAQ performance in 2016 (a 4\ub78-point difference), whereas differences between subnational locations with the highest and lowest HAQ Index values were more than two times as high for the USA and three times as high for England. State-level gaps in the HAQ Index in Mexico somewhat narrowed from 1990 to 2016 (from a 20\ub79-point to 17\ub70-point difference), whereas in Brazil, disparities slightly increased across states during this time (a 17\ub72-point to 20\ub74-point difference). Performance on the HAQ Index showed strong linkages to overall development, with high and high-middle SDI countries generally having higher scores and faster gains for non-communicable diseases. Nonetheless, countries across the development spectrum saw substantial gains in some key health service areas from 2000 to 2016, most notably vaccine-preventable diseases. Overall, national performance on the HAQ Index was positively associated with higher levels of total health spending per capita, as well as health systems inputs, but these relationships were quite heterogeneous, particularly among low-to-middle SDI countries. Interpretation GBD 2016 provides a more detailed understanding of past success and current challenges in improving personal health-care access and quality worldwide. Despite substantial gains since 2000, many low-SDI and middle- SDI countries face considerable challenges unless heightened policy action and investments focus on advancing access to and quality of health care across key health services, especially non-communicable diseases. Stagnating or minimal improvements experienced by several low-middle to high-middle SDI countries could reflect the complexities of re-orienting both primary and secondary health-care services beyond the more limited foci of the Millennium Development Goals. Alongside initiatives to strengthen public health programmes, the pursuit of universal health coverage hinges upon improving both access and quality worldwide, and thus requires adopting a more comprehensive view-and subsequent provision-of quality health care for all populations

Measuring progress and projecting attainment on the basis of past trends of the health-related Sustainable Development Goals in 188 countries: an analysis from the Global Burden of Disease Study 2016

The UN’s Sustainable Development Goals (SDGs) are grounded in the global ambition of “leaving no one behind”. Understanding today’s gains and gaps for the health-related SDGs is essential for decision makers as they aim to improve the health of populations. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016), we measured 37 of the 50 health-related SDG indicators over the period 1990–2016 for 188 countries, and then on the basis of these past trends, we projected indicators to 2030