エチオピアの農業協同組合、農村組織、農村金融機関に関する実証分析

博士（学術）神戸大

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

Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

As mortality rates decline, life expectancy increases, and populations age, non-fatal outcomes of diseases and injuries are becoming a larger component of the global burden of disease. The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016

Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970–2016: a systematic analysis for the Global Burden of Disease Study 2016

BACKGROUND: Detailed assessments of mortality patterns, particularly age-specific mortality, represent a crucial input that enables health systems to target interventions to specific populations. Understanding how all-cause mortality has changed with respect to development status can identify exemplars for best practice. To accomplish this, the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) estimated age-specific and sex-specific all-cause mortality between 1970 and 2016 for 195 countries and territories and at the subnational level for the five countries with a population greater than 200 million in 2016. METHODS: We have evaluated how well civil registration systems captured deaths using a set of demographic methods called death distribution methods for adults and from consideration of survey and census data for children younger than 5 years. We generated an overall assessment of completeness of registration of deaths by dividing registered deaths in each location-year by our estimate of all-age deaths generated from our overall estimation process. For 163 locations, including subnational units in countries with a population greater than 200 million with complete vital registration (VR) systems, our estimates were largely driven by the observed data, with corrections for small fluctuations in numbers and estimation for recent years where there were lags in data reporting (lags were variable by location, generally between 1 year and 6 years). For other locations, we took advantage of different data sources available to measure under-5 mortality rates (U5MR) using complete birth histories, summary birth histories, and incomplete VR with adjustments; we measured adult mortality rate (the probability of death in individuals aged 15-60 years) using adjusted incomplete VR, sibling histories, and household death recall. We used the U5MR and adult mortality rate, together with crude death rate due to HIV in the GBD model life table system, to estimate age-specific and sex-specific death rates for each location-year. Using various international databases, we identified fatal discontinuities, which we defined as increases in the death rate of more than one death per million, resulting from conflict and terrorism, natural disasters, major transport or technological accidents, and a subset of epidemic infectious diseases; these were added to estimates in the relevant years. In 47 countries with an identified peak adult prevalence for HIV/AIDS of more than 0·5% and where VR systems were less than 65% complete, we informed our estimates of age-sex-specific mortality using the Estimation and Projection Package (EPP)-Spectrum model fitted to national HIV/AIDS prevalence surveys and antenatal clinic serosurveillance systems. We estimated stillbirths, early neonatal, late neonatal, and childhood mortality using both survey and VR data in spatiotemporal Gaussian process regression models. We estimated abridged life tables for all location-years using age-specific death rates. We grouped locations into development quintiles based on the Socio-demographic Index (SDI) and analysed mortality trends by quintile. Using spline regression, we estimated the expected mortality rate for each age-sex group as a function of SDI. We identified countries with higher life expectancy than expected by comparing observed life expectancy to anticipated life expectancy on the basis of development status alone. FINDINGS: Completeness in the registration of deaths increased from 28% in 1970 to a peak of 45% in 2013; completeness was lower after 2013 because of lags in reporting. Total deaths in children younger than 5 years decreased from 1970 to 2016, and slower decreases occurred at ages 5-24 years. By contrast, numbers of adult deaths increased in each 5-year age bracket above the age of 25 years. The distribution of annualised rates of change in age-specific mortality rate differed over the period 2000 to 2016 compared with earlier decades: increasing annualised rates of change were less frequent, although rising annualised rates of change still occurred in some locations, particularly for adolescent and younger adult age groups. Rates of stillbirths and under-5 mortality both decreased globally from 1970. Evidence for global convergence of death rates was mixed; although the absolute difference between age-standardised death rates narrowed between countries at the lowest and highest levels of SDI, the ratio of these death rates-a measure of relative inequality-increased slightly. There was a strong shift between 1970 and 2016 toward higher life expectancy, most noticeably at higher levels of SDI. Among countries with populations greater than 1 million in 2016, life expectancy at birth was highest for women in Japan, at 86·9 years (95% UI 86·7-87·2), and for men in Singapore, at 81·3 years (78·8-83·7) in 2016. Male life expectancy was generally lower than female life expectancy between 1970 and 2016, an

Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

BACKGROUND: Measurement of changes in health across locations is useful to compare and contrast changing epidemiological patterns against health system performance and identify specific needs for resource allocation in research, policy development, and programme decision making. Using the Global Burden of Diseases, Injuries, and Risk Factors Study 2016, we drew from two widely used summary measures to monitor such changes in population health: disability-adjusted life-years (DALYs) and healthy life expectancy (HALE). We used these measures to track trends and benchmark progress compared with expected trends on the basis of the Socio-demographic Index (SDI). METHODS: We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 for all-cause mortality, cause-specific mortality, and non-fatal disease burden to derive HALE and DALYs by sex for 195 countries and territories from 1990 to 2016. We calculated DALYs by summing years of life lost and years of life lived with disability for each location, age group, sex, and year. We estimated HALE using age-specific death rates and years of life lived with disability per capita. We explored how DALYs and HALE differed from expected trends when compared with the SDI: the geometric mean of income per person, educational attainment in the population older than age 15 years, and total fertility rate. FINDINGS: The highest globally observed HALE at birth for both women and men was in Singapore, at 75·2 years (95% uncertainty interval 71·9-78·6) for females and 72·0 years (68·8-75·1) for males. The lowest for females was in the Central African Republic (45·6 years [42·0-49·5]) and for males was in Lesotho (41·5 years [39·0-44·0]). From 1990 to 2016, global HALE increased by an average of 6·24 years (5·97-6·48) for both sexes combined. Global HALE increased by 6·04 years (5·74-6·27) for males and 6·49 years (6·08-6·77) for females, whereas HALE at age 65 years increased by 1·78 years (1·61-1·93) for males and 1·96 years (1·69-2·13) for females. Total global DALYs remained largely unchanged from 1990 to 2016 (-2·3% [-5·9 to 0·9]), with decreases in communicable, maternal, neonatal, and nutritional (CMNN) disease DALYs offset by increased DALYs due to non-communicable diseases (NCDs). The exemplars, calculated as the five lowest ratios of observed to expected age-standardised DALY rates in 2016, were Nicaragua, Costa Rica, the Maldives, Peru, and Israel. The leading three causes of DALYs globally were ischaemic heart disease, cerebrovascular disease, and lower respiratory infections, comprising 16·1% of all DALYs. Total DALYs and age-standardised DALY rates due to most CMNN causes decreased from 1990 to 2016. Conversely, the total DALY burden rose for most NCDs; however, age-standardised DALY rates due to NCDs declined globally. INTERPRETATION: At a global level, DALYs and HALE continue to show improvements. At the same time, we observe that many populations are facing growing functional health loss. Rising SDI was associated with increases in cumulative years of life lived with disability and decreases in CMNN DALYs offset by increased NCD DALYs. Relative compression of morbidity highlights the importance of continued health interventions, which has changed in most locations in pace with the gross domestic product per person, education, and family planning. The analysis of DALYs and HALE and their relationship to SDI represents a robust framework with which to benchmark location-specific health performance. Country-specific drivers of disease burden, particularly for causes with higher-than-expected DALYs, should inform health policies, health system improvement initiatives, targeted prevention efforts, and development assistance for health, including financial and research investments for all countries, regardless of their level of sociodemographic development. The presence of countries that substantially outperform others suggests the need for increased scrutiny for proven examples of best practices, which can help to extend gains, whereas the presence of underperforming countries suggests the need for devotion of extra attention to health systems that need more robust support. FUNDING: Bill & Melinda Gates Foundation

LONG RUN DETERMINANTS OF CEREAL PRODUCTION IN ETHIOPIA: DOES CO2 EMISSION MATTER?

The study employed vector error correction model to examine the long run relationship between climate change and cereal production using time series data (1962-2014) in Ethiopia. The cointegrating equation shows that the parameters included in the model are jointly significant at 1% significant level. The study revealed that a 10% percent increase in CO2 emission would have 2.75 % cereal production loss in the country. However, land and fertilizer were found to have positive and significant long-term impact on cereal production in the country. The forecasted cereal production - using contingent equation – shows that cereal production is expected to grow annually by 2.8%, on average, for the next 10 years. As a concluding remark, efforts towards reducing CO2 should be strengthened to further enhance the cereal production growth in the country. Moreover, providing fertilizer for the farmers with a reasonable price on due time is decisive to benefit from intensive agriculture

Technical Efficiency of Major crops In Ethiopia: Stochastic Frontier Model

Production and productivity can be boosted either through increased use of inputs and/or improvement in technology or by improving the efficiency of producers or firms, given fixed level of inputs and technology. Even though agriculture stays the main stay of Ethiopian economy, level of agricultural productivity in general and crop productivity in particular is very low. Out of the total grain production in Ethiopia, cereals account for roughly 60 percent of rural employment and 80 percent of total cultivated land. However, Yield of cereals has been consistently well below world and even of least developing countries average yield, indicating poor productivity of the crops in the country. Given capital constraint in the country, it is difficult to adopt new technology to enhance productivity. Hence, working to improve production efficiency is best option on hand. As a result, there are a number of studies done on area of efficiency analysis in Ethiopia. However, the novelty of this study can be explained by three facts. First of all it has used national data, collected by International Food Policy Research Institute (IFPRI), with enough number of observations to do plot level analysis considering biological factors that determine inefficiency. Second, efficiency analysis is not based on a single crop rather on major crops in general as well as teff, wheat and maize independently. Last but not least, the study employed one stage approach in which both technical efficiency and factors of inefficiency are analyzed simultaneously. Therefore, this study was done to evaluate the efficiency and identify factors that explain the variation in inefficiency of crop production in Ethiopia. This study principally used the 2009 Ethiopia Rural Household Survey (ERHS) which is collected by IFPRI. As far as analysis is concerned, both descriptive and econometric methods were used. Descriptive statistics (mean, percentage, range, etc.) is used to summarize the variables in the model and describe the study area. Econometric model, Stochastic Production Frontier model, is used to estimate the elasticity of production function, determine the determinants of inefficiency and estimate the level of efficiency. Given that we are considering a developing country setting where by the main concern is output shortfall rather than input over use, preference has been given to primal or output oriented approach of measuring efficiency. In this study, effort was made to test the hypotheses before rushing to interpret the model outputs. First, the γ parameter estimates of all production functions were significant at 5% significance level, indicating Stochastic Frontier Production function is more appropriate than convectional production function or there is significant technical inefficiency variation among plots. The γ value of 0.636 for the major crops production function can be then interpreted as, 63% of the variation in output among plots is explained by technical inefficiency. Similarly, variation in out put due to technical inefficiency for teff, wheat and maize production were calculated to be 88.5, 45.5 and 77.8 percent respectively. The second step, following the existence of inefficiency, is to check if there exist one or more variables that could explain the variation in technical inefficiency. Log likelihood ratio was used to test the hypothesis. Accordingly, all calculated LL ratio values were greater than the critical value of LL ratio, with upper 5 % level of significance. Hence, the null hypotheses that determinant variables in the inefficiency effect model are simultaneously equal to zero are rejected. In other words, there exists at least one explanatory variable that explains the variation in the technical inefficiency among plots. The ML estimate results shown that, all variables were found to be binding in the production of major crops, meaning that an increase in one of inputs will enhance output keeping everything constant. As far as teff production is concerned, only land was a significant variable that explains the variation in teff output among plots. Land, DAP and seed were found to have significant and positive effect in wheat production. According to result of this study, land and seed were major determinants of maize production in Ethiopia. Generally, all significant input variables were found to affect output positively, as expected. Moreover, the model output depicted that the mean level of TE for major crops, Teff, Wheat and Maize production was found to be 63.56, 67.26, 84.16 and 91.41 percent, respectively. The inefficiency effect analysis shown that, age of the household head measured in years was found to be the determinant of technical inefficiency, of teff production and education was found to have negative and significant effect on major crops and wheat technical inefficiency (1% significance level). Knowledge about land policy was found to have significant and negative effect on technical inefficiency of wheat production (1% significance level). Similarly, participation in soil and water conservation activities was found to have negative and significant effect on technical inefficiency of major crops and wheat production. In this study frequency of extension contact was found to have unexpected and strange result; the more frequently the farmers meet extension workers the more it competes their time to do agricultural activities. The result of this study also confirmed as rich farmers are relatively less inefficient than poor once, in major crops production, and fertile plots of wheat are significantly less inefficient than infertile once. Similarly, flat teff and maize plots are more efficient than otherwise. The other plot specific variable that was found to have negative and significant effect on technical inefficiency of major crop production was adoption of improved seed. The last but not least, variable that explains variation in inefficiency was found to be livestock ownership. Generally, results of this study confirmed that there is a room to enhance productivity by improving the efficiency of production, given same level of input and current technology

Performance of Microfinance Institutions in Ethiopia: Integrating Financial and Social Metrics

Since their inception in the 1970s, microfinance institutions (MFIs) have received increasing attention both from policymakers and academic circles. Using unbalanced panel data (2000–2017) from Ethiopia, in this paper, we investigated the performance of MFIs and its determinants on the one hand and whether or not mission drift exists on the other hand. To this end, we employed seemingly unrelated regression (SUR) and fixed/random effect panel models. The results indicate that, based on different outreach and financial performance metrics, the MFIs in Ethiopia have good performance compared with those of the 10 biggest economies in Sub-Saharan Africa (SSA). The econometric estimation results show that asset holding and the yield on gross portfolio have a positive and significant effect on the social and financial performances of MFIs in Ethiopia. Furthermore, the number of loan officers, loan officer productivity, and personnel productivity have a positive and significant impact on the financial performance of MFIs. Our results also suggest that the null hypothesis—that MFIs are not shifting away from poorer clients—cannot be rejected, implying that there is no mission drift by MFIs in Ethiopia