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Cereal Markets in Ethiopia: Policies and Performances

Cereal Markets in Ethiopia: Policies and Performances



Ethiopian Development Research Institute(EDRI) and IFPRI Ethiopia Strategy Support Program 2 (IFPRI-ESSP2) Seminar Series

Ethiopian Development Research Institute(EDRI) and IFPRI Ethiopia Strategy Support Program 2 (IFPRI-ESSP2) Seminar Series
April 15, 2009



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    Cereal Markets in Ethiopia: Policies and Performances Cereal Markets in Ethiopia: Policies and Performances Presentation Transcript

    • Cereal Markets in Ethiopia: Policies and Performances Shahidur Rashid & Asfaw Negassa Prepared for the EDRI-IFPRI seminar Addis Ababa, April 15, 2009
    • Outline  Motivation  Cereal sub-sector is dominant within agriculture  Largest contributor to GDP  Largest employer  Heavy emphasis in the GoE’s growth strategies  Conceptual framework  Study findings  Policies and infrastructural development  Changes in structure  Changes in performance  Tentative conclusions
    • Conceptual framework Market Analysis Public Policies Policies Directly Indirectly Affecting Affecting Structure Markets: Markets: For example, For example, elimination of investments in Conduct movement infrastructure restrictions Performance Competitive Price Page 3
    • Policy reviews Review of Grain Marketing Policy Changes in Ethiopia: Objectives and key Observations Policy Regime Major Policy Key Observations Objective(s) Imperial Support and promote the Limited interventions and were not Regime interests of few landlords effective and urban consumers Socialist Complete socialization of Heavy government intervention Regime production and marketing which depressed the development of private grain trade The Current Price stabilization, Progresses have been made. Regime promote private sector However, good intentions are grain trade frustrated with ad hock nature of policy interventions
    • Policy reviews: key lessons  Ad hock nature of policy interventions  No sufficient details in the design and implementations  No sufficient resources assigned to implement the planned policy interventions  Policy not implemented or not effective  Created uncertainty in the market which affects private sectors optimal operational and investment decisions  Eroded public confidence in governments’ intervention measures  Grain marketing policies have been designed with too many objectives, which are often conflicting  For example, the EGTE has been expected to be commercially profitable while at the same time to meet social objectives of price stabilization under tight financial support from the government  Price stabilization requires sufficient working capital and stocks – adequate budgeting of policy interventions
    • Infrastructural developments Trends in road lengths for different classes of roads, 1951 to 2003 20000 18000 16000 14000 Length of roads (Km) 12000 10000 8000 6000 4000 2000 0 1951 1963 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 Asphalt Gravel Rural
    • Infrastructural developments (cont.) Warehouse_Nazereth Improvements in Ethiopian road infrastructure and accessibility, 1997 to 2005 Indicator 1997 2002 2005 Absolute change from 1997 to 2005 Proportion of paved roads in good 17% 35% 54% 37(+) condition Proportion of unpaved roads in good 25% 30% 40% 15(+) condition Proportion of regional roads in good 21% 28% 33.6% 12.6(+) condition Road density: length/1000 sq. km. 24.1km 30.3km 33.6km 9.5(+) Road density: Length/1000 0.46km 0.49km 0.51km 0.13(+) inhabitants Proportion of area more than 5 km 79% 75% 73% 6.0(-) from all-weather road Average distance to the road network 21.4km 17.0km 16km 5.4(-)
    • Infrastructural developments (cont.) Trends in number of fixed telephone lines and apparatuses, 1988 to 2003 500 450 400 350 Number ('1000') 300 250 200 150 100 50 0 Lines Apparatuses
    • Infrastructural developments (cont.) Trends in number of trucks of different sizes, 1993 to 2004 50000.00 40000.00 Trends in number of trucks of Number of trucks 30000.00 different sizes, 1993 to 2004 20000.00 10000.00 .00 Big Small all trucks
    • Infrastructural developments: key observations Warehouse_Nazereth  Significant improvements in marketing infrastructure  How these changes are affecting grain market performances?
    • Review of organization and structure of markets  Organization of Cereal Markets  Traditional and Emerging Cereal Marketing Channels  Broad Changes in Cereal Market Structure
    • Cereal value chain map involving traditional market channels Smallholders State farms Commercial farms Assemblers Wholesalers (surplus) Coops Brokers EGTE Wholesalers Export (deficit) Food aid agencies/WFP Processors Retailers/ Ration Shop Consumers
    • Cereal value chain map involving commodity exchange Smallholders State Commercial farms farms Assemblers Coops EGTE Wholesalers (surplus) Commodity exchange with brokers Wholesalers Exporters (deficit) Processors Food aid agencies Retailers Consumers
    • Structure (cont.) Market Key actors Key functions Recent changes level Production Smallholders, Production of cereals Re-emergence of private commercial farms, commercial farms and state farms Assembly Petty-traders, farmer Collection and Emerging cooperative marketing traders, cooperatives bulking of cereals Wholesale Large traders, Temporal and spatial •Emerging cooperative marketing Ethiopian grain trade arbitrage services •Emergence of ECX enterprise, •Emergence of Commodity Cooperatives Warehouse System Processing Small-scale, medium- Custom milling / New large-scale private entrants as scale, and large-scale commercial flour opposed to state-dominated flour mills mills, & processing sector under socialist manufacturing regime Retail Small traders, small- Sell cereals and flour Emergence of supermarkets scale flour mills, mills to consumers in carrying locally processed flour wholesale traders small quantities mills (mainly wheat) and imported cereal products
    • Performance  Price analyses (historical data)  Market integration  Seasonality  Price variability  Survey data analyses  Transactions costs  Trade margins
    • Performance: market integration concept  Consider the following facts:  In 1985, price of kg of teff was 7.7 Birr in Gojjam BUT 15.7 Birr in Wello  In 1974, price of rice in the district of Rangpur in Bangladesh (a deficit area) was almost three times the prices in surplus and well developed districts  What is common in these two cases?  Both countries had famines: Ethiopia in 1984/5 and Bangladesh in 1974.  Hard hit famine areas lacked integration with the surplus and well developed regions.  In both countries there were restrictions on grain movements
    • Performance: market integration review Author (s) Commodities Geographic coverage & time Method of analysis Findings periods Dadi, L., A. Negassa, and S. Maize and Teff Bako area of Western Shoa and Price correlation analysis Results indicate that private sector marketing of maize and teff is Franzel. 1992. Eastern Wollega characterized by high risk and variable gross margins. Interspatial arbitrage (1985 -1989) is serious flawed, correlations in prices range from weak to strong Dercon, S. 1995. Teff Ethiopia Modified Ravallion’s method Liberalization had important effects on the long-run and short-run (1987 – 1993) integration of markets: most teff markets were c- integrated with Addis Ababa market Negassa, A. 1996. Maize, teff, Bako area of Western Shoa and Price correlations, Granger’s Deregulation has resulted in an increase in real prices accompanied by an Noug and Eastern Wollega and Johansen’s co integration increase in price variability. Price correlation and Granger methods show Sorghum (1986 – 1993) methods improvement in market integration while Johansen method shows no significant changes. Negassa, A. and T. Jayne. 1997. Maize, teff, and Ethiopia Variance and price correlation Cereal price spreads have generally declined since reform. While prices in wheat (1985 – 1996) analyses surplus producing areas have risen by 12 – 48 percent; prices in deficit regions declined by 6 -36 percent. Getnet, K. Verbeke w. and J. Teff Ethiopia Autoregressive distributed lag Found long-run and short-run relationship between producer prices and the Viaene. 2005. (1996 – 2005) model wholesale price in major terminal market (Addis Ababa) Getnet, K., E. Gabre-Madhin, S. Wheat Ethiopia Granger cointegration and Some markets share a common factor but the price dynamics in the entire Rashid., and S. Tamiru. 2006. (1996 – 2006) error-correction and Johansen market considered are not as such influenced by single common factor. cointegration methods The implication is that different markets need different policy instruments to address the price stabilization issues Negassa, A. and R. Myers. 2007. Maize and Ethiopia Extended parity bounds model Grain market reform have improved spatial market efficiency in a few wheat (1996 – 2002) markets, worsened it in a few others, but generally to have had little effect on the spatial efficiency of Ethiopian grain market Rashid and Gabre-Madhin, 2007. Maize, wheat, Ethiopia Common trend and Most market locations, except one in the north and another in the eastern and teff (1996 – 2006) Multivariate co-integration part of the country, are integrated. Analyses of common trends indicate that analyses shocks to teff markets have little effects and shocks to maize markets have more persistent effects on the other commodities.
    • Performance: Seasonality and variability  Basics concepts of seasonality & variability analyses  Any time series variable can be represented as follows: X T C S I t  Where T = Trend component; C= Cyclical component  S = Seasonal component; and I = Irregular component  Decomposing these components is essential in seasonality and variability analyses!!
    • Performance: seasonality analyses  Centered moving averages eliminates Seasonal and Irregular components from the time series.  That is, CMX = T x C.  Dividing Xt by CMX leaves seasonal and irregular components.  By doing one more adjustment, the irregular component can be eliminated; and we are left with seasonal index.
    • Performance: seasonality Change of seasonality for teff wholesale Change of seasonality for maize price over time wholesale price over time 1.15 1.20 1.10 1.15 1.10 1.05 1.05 1.00 1.00 0.95 0.95 0.90 0.90 0.85 0.85 0.80 0.80 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Maize_2000s Maize_1980s Teff_2000s Teff_1980s Teff_1990s Maize_1990s
    • Performance: seasonality Change of seasonality for Wheat wholesale price over time 1.15 1.10 1.05 1.00 0.95 0.90 0.85 0.80 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Wheat_1980s Wheat_1990s Wheat_2000s
    • Performance: seasonality  Three things we can do with the estimated seasonality indices  Future price projection  Competitiveness in storage behaviors  Tests for the change in seasonality patterns  Future price projection  Suppose we want to project August price of teff in January based on the following info:  price of teff in January is 350 Birr /quintal  Seasonal indices for January & August are 0.95 & 1.1, respectively.  August price will roughly be ____!!!
    • Performance: seasonality  Competitiveness in storage  Suppose someone wants to make profits by buying maize when prices are low and selling when prices are high  Also suppose  The minimum and maximum seasonality indices are 0.93 and 1.1, respectively.  The trader need to store at least for six months to make profits  Maize loses 5 percent weight in six months  Trader borrowed money from bank at an interest rate of 12%. Is this trader’s storage competitive or is he making excess profit!!
    • Performance: seasonality Seasonality and storage 1.4 1.2 1 0.8 0.6 0.4 0.2 0 Teff Wheat Maize Sorghum Barley 2000s 1990s 1980s
    • Performance: variability  Three measures of variability Time Periods Measures of Cereals Variability a Maize Wheat Sorghu Barley Teff m 2000s Coefficient of Variation 71.33 53.45 59.82 60.95 51.27 Cuddy Le Valle Index 36.37 24.40 29.35 23.05 28.48 Coefficient of Variation 50.17 40.96 43.68 46.59 37.45 (based on MA series) 1990s Coefficient of Variation 23.01 16.81 20.05 17.75 16.00 Cuddy Le Valle Index 22.59 11.45 18.67 15.06 9.49 Coefficient of Variation 17.07 13.79 14.23 15.18 13.29 (based on MA series) 1980s Coefficient of Variation 41.91 31.95 31.54 28.45 24.67 Cuddy La Valle Index 41.79 31.18 30.07 28.37 24.39 Coefficient of Variation 34.72 24.54 26.66 21.14 18.92 (based on MA series)
    • Performance: costs and margins Costs and Margins 1996 2002 2008 Absolute change since 1996 2002 A. Transaction costs Total transaction costs (Birr/ ton) 323.57 123.14 54.58 -269.00 -68.57 Cost of handling 58.24 38.17 14.74 -43.51 -23.44 Cost of sacking 25.89 39.41 17.47 -8.42 -21.94 Cost of transport 100.31 25.86 8.19 -92.12 -17.67 Cost of storage 0.00 0.62 0.55 0.55 -0.07 Cost of road stops 16.18 0.49 0.00 -16.18 -0.49 Cost of brokers 25.89 11.08 -- -- -- Cost of travel 3.24 1.11 0.55 -2.69 -0.56 Cost of others 93.84 6.40 12.01 -81.83 5.60 B. Trade Margins Price difference (Birr/ton) 338.98 203.78 84.90 -254.08 -118.88 Gross margin rate (%)b -- 7 4 -- -3 Net margin (Birr/ton)c 77.04 58.22 30.32 -46.72 -27.90
    • What do estimates of costs and margins mean? Let’s do some math!!  What would have been the maize price in 2008 if there had been no change in transaction costs?  Total transaction costs in1996 was 28 percent of wholesale price  Price of maize in 1996 was 750 Birr per ton  In 2008, price of maize was 4170 Birr per ton; and transaction costs was less than 3 percent of the wholesale prices.  If transaction costs had remained the same, prices in 2008 would have been ______!!
    • THANK YOU!!!