The study considers the Ethiopian economy. It relied on secondary data (time series data) to investigate the impact of climate change on Ethiopian Agricultural GDP growth. The major sources of data for the study are the Ethiopian National Metrology Agency (NMA) and satellite data (TAMSAT) for climate variables, the National Bank of Ethiopia (NBE) for real agricultural GDP and the Central Statistical Agency (CSA), World Food and Agriculture Organization (FAO), World Bank Development Indicators Data Base, Ethiopian ministry of Agriculture for fertilizer, improved seed, agricultural land and labour force data. Depending on obtained data, the researcher used descriptive analysis to study the trend of variables over time and used the econometric model for detailed empirical analysis.
A yearly time series data on Real Agricultural Gross Domestic, Coefficient of variation of rainfall and mean monthly annual temperature (as an indicator for climate change), Land input used for agricultural production, total labour force employed, an area under fertilizer usage, and Improved seed usage is gathered covering the period from 1992/93 to 2017/18. The choice of the period is based on the availability of data. Relying on data on these variables, the econometric model is used to estimate the long-run and short-run elasticity of the variables.
In this study, to test the existence of a long-run relationship between the dependent variable AGDP and the other explanatory variables, the autoregressive distributed lag bound testing approach developed by Pesaran et al. (2001) is used. This approach is chosen since this technique has the advantage of being more efficient for studies with a small sample and applies to series that are integrated of order 1, level 0 or mutually cointegrated, unlike the traditional tests such as Engle and granger, Johansen and Juselius, etc.
Following the literature (particularly Eberhardt and Teal, 2012), a Cobb-Douglas production function can be applied to analyze the impact of climate change on agricultural production output. Thus, to quantify the impact of climate change on the economic performance of Ethiopian agriculture, this analysis uses the Autoregressive distributed lag approach on the following empirical production function, which relates agricultural output to the various factors of production.
The functional equation is based on the assumption that a country's agricultural production is a function of technology, capital, labour and climate. Based on the extant literature, a model is specified to perform the relationships between Real Agricultural Gross Domestic Product, Coefficient of variation of annual mean monthly temperature and Coefficient of variation of mean annual rainfall. Since Agricultural Production broadly is a function of capital, labour, and technology; Land input used for agricultural production, total labour force employed, fertilizer usage and improved seed usage are included in the model as control variables. That is: -
RAGDP = f (CVT, CVR, LD, FA, IS, LF)
The model employed in this study can be written as follows.
![](data:image/png;base64,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)
The coefficient of variation (CV) is defined as the ratio of the standard deviation to the mean. It shows the extent of variability with the mean of the population. According to Hare (2003), CV is used to classify the degree of variability as less (CV < 20), moderate (20 < CV < 30), and high (CV > 30).
Coefficient of variation as a measure of climate change-induced variability is used since it includes two commonly used measures of temporal climate variability, mean and standard deviation, in one value. The mean value has some drawbacks in providing full information about a population's true characteristics since it is highly affected by extreme values. The coefficient of variation gives an estimate of variability around the average around 67% of the time. It is a measure of relative variability.
The Rainfall and temperature data used to calculate the coefficient of variation are national-level data and annual data is used in the computation is satellite data. But to further analyze the consistency of the empirical result coefficient of variation in each synoptic station (those presenting a general view of the whole country) is computed and the average result is taken in the regression analysis. Furthermore rainfall and temperature coefficient of variation of the country's main wet season, the Meher season has been calculated and analyzed to check the robustness of the empirical result.