1. Climate change impact on Ethiopian small-holders production efficiency Solomon Asfaw, FAO, Agricultural Development Economics Division (ESA) Sabrina Auci, University of Palermo, European Studies and International Integration Department (DEMS) Manuela Coromaldi, University of Rome “Niccolò Cusano” International Consortium on Applied Bioeconomy Research – ICABR 2015

2. Building blocks Motivation Country background and climate variability Data description Empirical strategy Estimation results Conclusions

3. Motivation (1) Many of the anticipated adverse effect of climate change such as increases in global temperature, sea level rise, enhanced monsoon precipitation and increase in drought intensity aggravate the development of agriculture-based economies such as Sub-Saharian countries ; the objective of this study is to provide a comprehensive analysis of the impact of weather/climate risk on farmers’ technical efficiency; analysing the climate change effects on this country is very interesting because it presents different microclimates.

4. Motivation (2) Besides the important policy implications that can be derived from the investigation of these issues, we focus on weather-related risk for two reasons: 1)the growing availability of high quality geo-referenced data on weather makes this important; 2) although it is not the only exogenous factor affecting income and consumption of rural households, it is spatially covariant.

5. Country background Ethiopia, like many other sub Saharan countries, weightily relies on the agricultural sector and consequently the economic impact of climate change is crucial for small-scale farmers’ food security and welfare; the agriculture sector contributes about 46 percent of total GDP; agricultural production is completely dependent on rainfall (85% of population dependent on rain-fed agriculture for their livelihood (ACCRA, 2010)).

6. Climate variability Since the 1970s the severity and the frequency of drought have increased and the areas affected by drought and desertification are expanding (World Bank, 2013). Major floods, which caused significant damage, with numerous livestock deaths and damage to planted crops and stored food, occurred in different parts of the country in 1988, 1993, 1994, 1995, 1996, 2006 and 2012 (NAPA, 2007; UN OCHA-Ethiopia, 2013). Regional projections of climate models not only predict a substantial rise in frequency of both extreme flooding and droughts, but also suggest an increase in mean temperatures over the 21 st century due to global warming (EACC, 2010).

7. Data description Two main sources of data:  socio-economic data from Ethiopia Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) 2011/2012;  historical re-analysis data on rainfall and temperature from the National Oceanic and Atmospheric Administration (NOAA) and the European Centre for Medium Range Weather Forecasts (ECMWF), respectively.

8. Table 1a. Descriptive statistics for the overall dataset

9. Table 1b.Descriptive statistics by Ethiopian regions

10. Empirical strategy (1) The use of exogenous variables such as climatic variables in the stochastic frontier approach allows to capture the effects of the “environment” in which a farmer produces his agricultural output (Kumbhakar and Knox Lovell, 2000). We include the climatic variables in the inefficiency model to estimate technical efficiency. The incorporation of exogenous variables within the estimation of technical efficiency means the incorporation of features beyond the control of the farmer and the separation of the production frontier model from the inefficiency equation.

11. Empirical strategy (2) This approach estimates simultaneously the production input coefficients and efficiency/inefficiency factor parameters using a two-stage maximum-likelihood procedure (Kumbhakar et al., 1991; Reifschneider and Stevenson, 1991; Huang and Liu, 1994). The production function model can be expressed as: (1)Y i = x i β + (v i - u i ) i=1,..., N where v i  N(0,  v 2 ) and u i is distributed as truncated normal random variable N(m i,  u 2 ).

12. Empirical strategy (3) The mean of this truncated normal distribution is a function of systematic variables that can influence the efficiency of a farmer: (2)m i = z i  + ε i where z i is a px1 vector of variables that may have an indirect effect on the production function of an Ethiopian household;  is a 1xp vector of parameters to be estimated and ε i is defined by the truncation of the normal distribution with zero mean and variance  2 

13. Model specification (1) The Cobb-Douglas production function can be specified as follows: The non- homogeneous translog production function can be specified as follows:

14. Model specification (2) The inefficiency model can be specified as follows: mean_tma: the average of March-September maximum temperature average for the period 1989-2010 by enumeration; sd_tma: the standard deviation of March-September maximum temperature average for the period 1989-2010 by enumeration area; mean_rain: the average of March-September rainfall average for the period 1983-2012 by enumeration area; sd_rain: the standard deviation of March-September rainfall average for the period 1983-2012 by enumeration area.

15. Technical efficiency The technical efficiency of the i-th Ethiopian household using the Battese and Coelli (1988) estimation is given by : Technical in/efficiency values will oscillate between 0 and 1. If TE i <1 then the observable output is less than the maximum feasible output, meaning that the statistical unit is not efficient.

16. Table 3.Tests for functional form of the production function

17. Table 2.Stochastic frontier estimation results

18. Table 2.(continued)

19. Table 4.ANOVA Analysis of technical inefficiency by Ethiopian regions

20. Conclusion (1) This paper contributes to the climate change literature: by using a novel data set that combines information coming from two large-scale household surveys with geo-referenced historical rainfall and temperature data; by estimating the technical efficiency of Ethiopian households in the period 2011-12; by analysing the climate change effects on farmers’ efficiency taking into account the heterogeneity of climate areas (desert, tropical forest and plateau); by ranking the Ethiopian regions from the less efficient to the most efficient.

21. Conclusion (2) The results of the production functions are generally conform to our expectations and to literature; In the inefficiency model, we find that long term climatic variables computed on precipitations and maximum temperatures in the cropping season affect positively farmers’ efficiency; Regions in the east side of the country (suc as Somalie and Oromiya) are less efficient than regions in the west side (such as Benishangul Gumuz or Gambella).

22. Further developments We calculated a series of intra- and inter-seasonal indicators to measure both the level and variability of rainfall and maximum temperature: intra-seasonal indicators measure weather fluctuations such as Walsh and Lawler (1981) Seasonality Index for precipitations; inter-seasonal indicators measure the climate of a specific area such as Coefficient of Variation computed for the past 10, 5 and 3 years.