Climate change impact on Ethiopian small holders production efficiency

Transcription

1 Fifth IAERE Annual Conference February 2017, Rome 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 Relations Department (DEMS) Manuela Coromaldi, University of Rome Niccolò Cusano

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

3 Motivation (1) Many of the anticipated adverse effect of climate change (CC) 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 aim of this study is to provide an analysis of the impact of weather risk on small farmers technical efficiency climate change effects analysis of Ethiopia is interesting because of the presence of different microclimates 3

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 the following two reasons: the growing availability of high quality geo-referenced data on weather CC is one of the most important exogenous factor affecting income and consumption of small rural households 4

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

6 Climate variability Since the 1970s the severity and the frequency of droughts have increased and the areas affected by drought and desertification are expanding (World Bank 2013, 2014) Major floods, which caused significant damage, with numerous livestock deaths and damage to planted crops and stored food, occurred in different parts of Ethiopia 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) 6

7 Data description Two main sources of data: socio-economic data from World Bank Ethiopia Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) 2011/2012 and 2013/2014; Historical analysis data on rainfall and temperature from the National Oceanic and Atmospheric Administration (NOAA) and the European Centre for Medium Range Weather Forecasts (ECMWF). 7

8 Table 1. Descriptive statistics Model Var Description mean p50 sd min max N Production function inputs Inefficiency model factors Y L Chemfert Seed H Rainfall_sd Tmax_sd Yields=Crop harvested (maize, sorghum, barley, teff, wheat) /Operated land (Kg/Ha) Days of work employed / Operated land Use of Chemical Fertilizer in kilos / Operated land (Kg/Ha) Seeds used in kilos / Operated land (Kg/Ha) Average years of education in HH Standard deviation of rainfall in the last 5 year Standard deviation of maximum temperature in the last 5 year 26, , , ,248, , , , ,

9 Table 2. Descriptive statistics by Ethiopian regions (mean value) Region Y L Chemfert Seed H Rainfall_sd tmax_sd Tigray 34, Afar 38, Amhara 19, Oromiya 27, Somalie 13, Benishangul Gumuz 37, SNNP 26, Gambella 50, Harari 24, Dire Dawa 24,

10 Empirical strategy (1) The applied SF models allow : to control for time-invariant inefficiency (Pitt and Lee, 1981) to disentangle time-invariant heterogeneity from time-varying inefficiency ( true fixed- and random- effects; Greene, 2005) to control for territorial heterogeneity clustering our estimation by regions to control for heteroskedasticity: specifying explanatory variables for the inefficiency variance function (Greene, 2007) 10

11 Empirical strategy (2) The use of exogenous variables such as climatic variables in the stochastic frontier approach (SFA) allows to capture the effects of the environment in which a farmer produces his agricultural output (Kumbhakar and Knox Lovell, 2000) We include the weather 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

12 Empirical strategy (3) Pitt and Lee (1981) assume that the inefficiency term is farmer-specific so that technical efficiency is constant over time (time-invariant model). The production function can be expressed as: (1) Y it = + x it +(v it u i ) i=1,..., N and t=1,, T where v it ~ N(0,σ v2 ) and u i is distributed as a half-normal random variable N + (0,σ u2 ). 12

13 Empirical strategy (4) The limitation of the time-invariant model can be overcome following Greene (2005): a time-varying SFA with a household-specific fixed or stochastic term i is applied. The simultaneous estimation of production input coefficients and inefficiency factor parameters is based on a two-stage maximum-likelihood procedure (Kumbhakar et al., 1991). The production function can be expressed as: (2) Y it =x it +(v it -u it )+ i i=1,..., N and t=1,, T where v it ~ N(0,σ v2 ) and u it is distributed as a half-normal random variable N + (0,σ u2 ). 13

14 Empirical strategy (5) To analyze the inefficiency determinants related to observed heterogeneity of Ethiopian households which directly affects the inefficiency u it, we include efficiency determinants in the variance of the inefficiency error as in Greene (2007) and Hadri et al. (2003): (3) 2 uit exp z it where z it is a px1 vector of variables that may have an indirect effect on the production function of an Ethiopian household; γ is a 1xp vector of parameters to be estimated 14

15 Model specification (1) The standard the Cobb-Douglas (C-D) production function is applied: lny it = 0 lnseed 4 + lnl it 1 + v it it lnh u it 2 i it lnchem_fer 3 t it where Y it is the yield per hectare in kilos L it are days of work employed H it is average years of education in HH Chem_fert it is chemical fertilizer in kilos Seed it is the amount of seeds used in kilos 15

16 Model specification (2) The inefficiency model can be specified as follows: 2 it exp 0 + 1Rainfall_sd it + = Tmax_sd 2 itit it Rainfall_sd: the standard deviation of September-March rainfall average for the last five years by enumeration area Tmax_sd: the standard deviation of September-March maximum temperature average for the last five years by enumeration area Following Dell, Jones, and Olken (2012), we take the lags of weather variability 16

17 Technical efficiency The technical efficiency of the i-th Ethiopian household using the Battese and Coelli (1988) estimation is given by : TE it e ( u it ) ( z ti ti e ) 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 17

18 Table 3. Stochastic frontier estimation results PITT and LEE (1981) TFE without inefficiency model (GREENE, 2005) TRE without inefficiency model (GREENE, 2005) TFE with inefficiency model (GREENE, 2007) TRE with inefficiency model (GREENE, 2007) Production Function Frontier Model lnl 0.567*** 0.560*** 0.369*** 0.432*** 0.351*** (6.697) (3.39) (4.44) (2.63) (4.362) lnseed 0.211** (2.038) (0.575) (0.675) (1.597) (0.677) lnqchem_fert 0.184*** 0.229*** 0.140*** 0.234*** 0.148*** (3.517) (3.098) (2.842) (4.729) (3.01) H *** *** *** *** *** (2.901) (5.605) (3.04) (3.816) (3.117) Constant 4.212*** 5.850*** 5.996*** (31.2) (20.51) (20.09) Note: t statistics in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1% 18

19 Table 3. Stochastic frontier estimation results (continued) Inefficiency model Rainfall_sd ** *** (-2.127) (-4.049) Tmax_sd 0.648** 1.554*** (2.268) (3.442) Constant *** (-1.38) (-2.982) Obs 4,366 4,366 4,366 4,366 4,366 N. of households 2,183 2,183 2,183 2,183 2,183 Wald chi *** 3092*** 783.3*** 1422*** 570.4*** Prob > chi N_clust Log Likelihood sigma_u sigma_v avg_sigmau Note: t statistics in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1% 19

20 Table 4. ANOVA Analysis of technical inefficiency by Ethiopian regions True random effects model without inefficiency model True random effects model with inefficiency model Region Code Mean efficiency Region Code Mean efficiency Harari Gambella Oromiya Benishangul Gumuz SNNP Tigray Dire Dawa SNNP Tigray Oromiya Amhara Somalie Benishangul Gumuz Amhara Somalie Afar Afar Harari Gambella Dire Dawa ANOVA test F Prob > F

21 Conclusion (1) This paper contributes to the CC 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 and by analyzing the CC determinants on farmers efficiency taking into account the heteroskedasticity by considering the heterogeneity of climate areas (desert, tropical forest and plateau) by ranking the Ethiopian regions from the less efficient to the most efficient 21

22 Conclusion (2) The results of the production functions are generally conform to our expectations and to literature In the inefficiency model, we find that short-term weather variables computed on precipitations and maximum temperatures in the cropping season affect negatively and positively Ethiopian farmers inefficiency respectively Regions in the west side of the country (such as Tigray, Benishangul Gumuz, Gambella) are less efficient than regions in the north-west side (such as Afar, Dire Dawa, Harari) 22

23 Further developments A series of intra- and inter-seasonal indicators to measure both the level and the variability of rainfall and maximum temperature should be calculated: o intra-seasonal indicators measure weather fluctuations such as Walsh and Lawler (1981) Seasonality Index for Precipitations o inter-seasonal indicators measure climate shocks such as Coefficient of Variation computed for the past 10, 5 and 3 years o shortfall indicators measure how much less rain fell in the year t during the cropping season compared to average of the last 25, 10, 5, 3 years 23

24 Thank you! 24

25 Table 3. Stochastic frontier estimation results PITT and LEE (1981) True Random Effects (TRE) without inefficiency model (GREENE, 2005) True Random Effects (TRE) with inefficiency model (GREENE, 2007) Production Function Frontier Model lnl 0.567*** 0.567*** 0.351*** (6.697) (6.696) (4.362) lnseed 0.211** 0.211** (2.038) (2.037) (0.677) lnqchem_fert 0.184*** 0.184*** 0.148*** (3.517) (3.518) (3.01) H *** *** *** (2.901) (3.117) Constant 4.212*** 4.214*** 5.996*** (31.2) (20.09) Note: t statistics in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1% 25

26 Table 3. Stochastic frontier estimation results (continued) Inefficiency model Rainfall_sd 0.125*** ( 4.049) Tmax_sd 1.554*** (3.442) Constant 4.206*** ( 2.982) Obs 4,366 4,366 4,366 N. of households 2,183 2,183 2,183 Wald chi *** *** 570.4*** Prob > chi N_clust Log Likelihood sigma_u sigma_v avg_sigmau Note: t statistics in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1% 26