Since both inflation and economic growth are not a new concept rather their relationships are waited still now as a debatable issue among macro-economists, policy makers, policy analysts, politicians and even the population itself by giving their own analysis by conduct a research and assumption based on the trend as before. Basically, the aims of this seminar paper are to review the relationship between inflation and economic growth as well as to review the causes, sources, determinants and impacts of Ethiopian inflation. Most of the studies indicated above shown that, higher and volatile inflation is bad for the economy. On the other hand, lower and stable inflation is considered as a promoter of the economy. Then the question should focus on what level of inflation is harmful to economic growth? Many economists have made researches on estimating the threshold level of inflation using panel data for a number of countries and time-series data for single country cases and these researchers fix the threshold level of inflation for both developing and developed country. But in this seminar paper, quantifying or fix the exact number of threshold level of Ethiopian inflation and decide below this level inflation has a positive effect on growth and beyond this level it has negative impact on growth is very difficult by simply review previous literature without conducting actual research and make a deep analysis. Even if it is the case, based on the literature it is surely possible to conclude the inflation rate has a serious negative effect on the growth of one country’s economy especially in Ethiopia, if inflation has a double digit of an annual growth.

This study analyzed the economic efficiency of smallholder farmers in barley production in the case of Meket district, Amhara National Regional State, Ethiopia. A cross sectional data collected from a sample of 123 barley producers during the 2015/2016 production season was used for the analysis. Two stages random sampling method was used to select sample respondents. The translog functional form was chosen to estimate both production and cost functions and OLS estimation method was applied to identify allocative and economic inefficiencies factors, while technical inefficiency factors were analyzed by using single stage estimation approach. The estimated stochastic production frontier model indicated input variables such as fertilizer, human labor and oxen power as significant variables that increase the quantity of barley output, while barley seed had a negative effect. The estimated mean levels of technical, allocative and economic efficiencies of the sample farmers were about 70.9, 68.6 and 48.8%, respectively which revealed the presence of a room to increase their technical, allocative and economic efficiencies level on average by 29.1, 31.4 and 51.2%, respectively with the existing resources. Among the hypothesized factors expected to affect technical, allocative and economic inefficiencies, extension contact and number of barley plots significantly and negatively affected all inefficiencies level. Besides, distance of residence from the nearest main market was found to have a positive and significant effect on all inefficiencies of sampled farm households. Hence, emphasis should be given to decrease the inefficiency level of those more inefficient farm households via experience sharing among the better of farmers and usage of improved or certified barley seed. Besides this, policies and strategies of the government should be directed towards increasing farmers’ education, improve the system of input distributions and institutional facilities. Key words: Economic efficiency, stochastic frontier, trans-log, ordinary least square, Meket, barley.

Flooding remains a major environmental hazard in the Lower Awash River Basin (ARB), disproportionately affecting agropastoral households. This study integrates GIS‑based flood hazard mapping with the IPCC‑aligned Livelihood Vulnerability Index (LVI‑IPCC) to identify exposure hotspots and socioeconomic susceptibility in Afambo, Aysaita, and Dubti districts. Flood hazard was mapped using multi‑criteria decision analysis (MCDA‑AHP) with seven biophysical variables, while 294 households were selected using probability proportional to size and systematic sampling. Model validation using the 2021 Global Flood Monitoring event showed that 52% of observed flooded areas overlapped with very‑high hazard zones and 26% with high zones. About 57% of the basin faces high (34%) or very high (23%) flood susceptibility, concentrated along the Awash River floodplain. Notably, this hazard prediction found valid terms of observed flood events of 2020, where 78% of actual flood extents were aligned with the high to very high level of susceptibility areas. LVI‑IPCC results showed Dubti as the most vulnerable district (IPCC‑VI = − 0.083), driven by high exposure and sensitivity and comparatively lower adaptive capacity. Aysaita exhibited severe physical flood exposure, while Afambo’s vulnerability stemmed from limited adaptive resources. Key drivers include proximity to rivers, low elevation, and gentle slopes (0–0.5°). Socioeconomically, financial inclusion, irrigation access, and diversified incomes significantly reduced vulnerability. We recommend district-specific flood management: Dubti requires financial empowerment, Aysaita needs expanded irrigation infrastructure, and Afambo warrants enhanced early-warning systems. The study concludes that integrating spatial hazard information with livelihood dimensions provides a robust basis for building resilience in flood‑prone dryland ecosystems.

The agricultural sector in Ethiopia is primarily characterized by smallholder farming, which suffers from low productivity. Climate change is also one of the major constraints that hampers crop productivity. This study aims to analyze the current and future impacts of climate change on agricultural production using current data and the latest climate change scenarios. Panel data from the fourth (2018/19) and fifth (2021/22) waves of the Ethiopian Socioeconomic Survey were utilized. The National Aeronautics and Space Administration Prediction of Worldwide Energy Resources and the World Climate Research Program websites served as the main sources of historical and simulated climate data. The Beijing Climate Center Climate System Model, Community Earth System Model version 2, the Meteorological Research Institute Earth System Model version 2.0, and Medium-resolution version of the Norwegian Earth System Model version 2 Global Circulation Models were employed to get the simulated climate data from 2015 to 2100 in Ethiopia under four different scenarios. The output from the Ricardian model indicates that annual temperature has a statistically significant negative impact on both current and future crop production in Ethiopia, while annual precipitation has a positive impact. The study also reveals that current and future climate change has a heterogeneous impact on various types of agro-ecologies, while exhibiting a homogeneous impact on six major cereal crops. Therefore, policymakers should design appropriate climate change mitigation and adaptation strategies to reduce both the causes and effects of climate change, respectively.

This paper is aimed to decompose, the economic growth of the Afar national regional state of Ethiopia in to labor productivity, employment rate and working age population and to verify whether the positive economic growth registered earlier on the region was a job creation growth or not. To do so, annual time series data ranging from 2010 to 2018 were used and per capita value added as a measure of economic growth was decomposed by using Shapley decomposition analysis technique. The result reveals that changes in output per worker accounted the highest proportion (74.33%) followed by changes in working age population (32.85%) and employment rate (−7.17%) for the total change in per capita value added during the entire period of time. The negative growth changes registered in employment rate implied that the economic growth registered on the Afar national regional state was a jobless growth. The economic growth patterns of Afar region does not favor the agriculture sector where the poor are found. The within-sector takes the lion share contributing (72.67%) to aggregate change in output per worker growth followed by structural change (13.21%) and interaction of within sectors and structural change (14.11%). Finally, these findings navigated us to suggest for policymakers, to amend their sector-wise economic policies and strategies focused to enhance employment generation and labor productivity of the Afar region.