Dynamic Implications of Production Shocks and Policy on Livestock Markets and Household Welfare: A Sectoral and Economy-Wi...
Outline <ul><li>This seminar meant to introduce the project, present some preliminary results,  get early feedback </li></...
Introduction <ul><li>A lot of micro and value chain analysis of livestock in Ethiopia, but little macro-analysis </li></ul...
Introduction <ul><li>So this study is multifaceted. At the moment we have completed a demand analysis, started a supply/pr...
Need for better macro-understanding <ul><li>A lot of micro and value chain analysis of livestock in Ethiopia, but little m...
Macro-picture and link with policy <ul><li>Construct a macro-picture of the livestock sector </li></ul><ul><li>Understand ...
Figure x. Overview of the research components and their linkages with each other Household demand analysis of livestock Ge...
Market analysis – supply constraints <ul><li>Goal here is to understand how the various livestock markets and production s...
An attempt to be diagnostic Beef Shoats Poultry Dairy Comments Feed XXX XXX X XXX Regional & seasonal variation based on f...
Market analysis – supply constraints <ul><li>If we roughly agree (?), what are the implications? </li></ul><ul><li>Supply ...
Market analysis - prices <ul><li>How are livestock prices formed? </li></ul><ul><li>Important for understanding production...
Price formation situation <ul><li>2. Price formation, including integration with international markets </li></ul><ul><li>C...
Changing composition of livestock exports – fall in hides, big rise in live bovine
Big rise in live animal export values, almost certainly related to increase international prices
But note the changing composition of trade routes and end markets: Djibouti down and Somalia and Sudan up (formal trade re...
Price formation  <ul><li>Research Objectives  </li></ul><ul><li>Assess if and how regional and central livestock markets a...
Objectives and a Brief Description of Proposed Study…contd. <ul><li>Basic idea behind TAR: </li></ul><ul><ul><li>To invest...
Data Description   <ul><li>Market integration:  </li></ul><ul><ul><li>CSA price data spanning July 2001-January 2011, for ...
Data Description … contd.
Data Description … contd. <ul><li>On factors affecting price formation monthly data collected on: </li></ul><ul><ul><li>In...
Data  Description … contd.
Data  Description … contd.
Observations: <ul><li>Some markets seem to be integrated with local markets others with neighboring countries. </li></ul><...
<ul><ul><li>Nationally representative demand elasticity estimates for livestock products in Ethiopia are unavailable. </li...
<ul><li>Livestock products account for 4.4% of total household expenditure and 8.7% of food expenditure. </li></ul><ul><ul...
<ul><li>Expenditure on meat represents the largest expenditure group followed by dairy products  </li></ul><ul><ul><li>Sha...
<ul><li>Per Capita Quantity Consumed (kg) </li></ul><ul><li>Source: Authors’ calculation using HICES (2004/05) </li></ul><...
<ul><ul><li>Individuals in urban areas are likely to consume about three times as much as their rural counterparts. </li><...
<ul><li>Per capita calorie intake </li></ul><ul><ul><li>Source: Authors’ calculation using HICES (2004/05) </li></ul></ul>...
<ul><ul><li>People in the fifth exp. quintile get three times more calorie than those in the first quintile from livestock...
<ul><li>Data </li></ul><ul><ul><li>Nationally representative survey datasets for 2004/05 – Household Income, Consumption, ...
<ul><li>Expenditure Elasticities </li></ul><ul><li>Standard Errors in Brackets; ***,**,* are significance level at 1%, 5% ...
<ul><li>Expenditure elasticities appear to be higher in rural areas than in urban areas. </li></ul><ul><ul><li>The gap bet...
Results (cont’d) – Compensated own-Price and cross-price Elasticities Beef Mutton & Goat meat Other meat & animal products...
<ul><li>All own price effects are negative and significant. </li></ul><ul><ul><li>Mutton/goat meat has the highest own pri...
<ul><ul><li>Nationally representative demand elasticity estimates for livestock products in Ethiopia are unavailable. </li...
<ul><li>Livestock products account for 4.4% of total household expenditure and 8.7% of food expenditure. </li></ul><ul><ul...
<ul><li>Expenditure on meat represents the largest expenditure group followed by dairy products  </li></ul><ul><ul><li>Sha...
<ul><li>Per Capita Quantity Consumed (kg) </li></ul><ul><li>Source: Authors’ calculation using HICES (2004/05) </li></ul><...
<ul><ul><li>Individuals in urban areas are likely to consume about three times as much as their rural counterparts. </li><...
<ul><li>Per capita calorie intake </li></ul><ul><ul><li>Source: Authors’ calculation using HICES (2004/05) </li></ul></ul>...
<ul><ul><li>People in the fifth exp. quintile get three times more calorie than those in the first quintile from livestock...
<ul><li>Model -  the QU-AIDS (Banks, Blundell and Lewbel (1997)). </li></ul><ul><li>Restrictions </li></ul>Methodology - M...
<ul><ul><li>Zero expenditures/consumption   </li></ul></ul><ul><ul><ul><li>Problem : Such censoring can produce biased and...
<ul><li>Data </li></ul><ul><ul><li>Nationally representative survey datasets for 2004/05 – Household Income, Consumption, ...
<ul><li>Expenditure Elasticities </li></ul><ul><li>Standard Errors in Brackets; ***,**,* are significance level at 1%, 5% ...
<ul><li>Expenditure elasticities appear to be higher in rural areas than in urban areas. </li></ul><ul><ul><li>The gap bet...
<ul><li>Compensated Price Elasticities </li></ul><ul><li>Standard Errors in Brackets; ***,**,* are significance level at 1...
<ul><li>All own price effects are negative and significant. </li></ul><ul><ul><li>Mutton/goat meat has the highest own pri...
 
 
 
 
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Dynamic implications of production shocks and policy on livestock markets and household welfare a sectoral and economy wide analysis

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Ethiopian Development Research Institute (EDRI) and International Food Policy Research Institute (IFPRI/EDRI), Semiar Series, April 20, 2011

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  • Research Objectives Being part of a larger project that is aimed at examining a sector little studied this section of the study is aimed at assessing if and how regional and central markets are integrated, and Identify factors that play significant role in affecting livestock price formation and study how these factors influence livestock pricing and possibly study if and how they affect trade routes To achieve these objectives we propose: Integration of markets is intended to be investigated using threshold autoregression model We are gathering data relevant to study price formation and surveying what econometric model to use
  • The basic idea behind applying TAR to analyze market integration, which somebody called “a fancy way of doing correlations”, is to see if and how prices in spatially separated markets co-move and how fast price shocks are transmitted among integrated markets TAR differs from simple correlation as it acknowledges the existence of thresholds, which are created by transaction costs, that price differentials must exceed before equilibrating price adjustments that lead to market integration occur. Simple representation of a TAR model for two spatially separated livestock markets ( i and j ) with prices P i t and P j t , both of which are unit root AR(1) is: The regime switching framework can be characterized as: where c is the threshold value that causes a regime switch. Specifically, when the lagged price differential is below the threshold value lambda 1 =1, implying that the parity relationship follows a random walk when there are small deviations of price differences. However, a large deviation, such as a shock to the price in either market, will trigger the condition j ~ Pt􀀀1j &gt; c, causing = (2). Under the assumption that a stable equilibrium between prices at the two spatially separated locations exists, lambda 2 &lt; 1, implying that the price differential process is stationary and shocks to P i t or P j t will die out over time.
  • For MI analysis although there were longer time series CSA data that we found less reliable and ILRI data that was rich in terms its detail in livestock quality with short time series and lacking information on many markets we preferred to use monthly CSA price data that balances both quality and time span. The data spans from July 2001 through January 2011 (115 months). It comprises price data on 4 categories of livestock, goats, sheep, cock, and hen. Although we are working on developing some weight for markets the data shows that un-weighted average real prices have increased by an average annual rate of 3.4, 1.9, 3.5, 2.3, 1.1, 2, 3.4, and 3.5 percent for bulls, cows, oxen, heifer, sheep, goats, cocks, and hen, respectively.
  • As can be expected the story is different when considering nominal prices. The same 8 categories had average annual growth in nominal prices of 17.6, 16.1, 17.8, 16.4, 14.8, 15.9, 17.8, and 17.7 percent.
  • While correlations with real prices are weak, nominal prices of livestock are strongly correlated with almost all of the variables
  • Dynamic implications of production shocks and policy on livestock markets and household welfare a sectoral and economy wide analysis

    1. 1. Dynamic Implications of Production Shocks and Policy on Livestock Markets and Household Welfare: A Sectoral and Economy-Wide Analysis IFPRI-ILRI-EDRI Livestock Initiative Informal seminar, April 20, 2011
    2. 2. Outline <ul><li>This seminar meant to introduce the project, present some preliminary results, get early feedback </li></ul><ul><ul><li>Introduction </li></ul></ul><ul><ul><li>Market analysis </li></ul></ul><ul><ul><li>Demand analysis </li></ul></ul><ul><ul><li>Discussion </li></ul></ul>
    3. 3. Introduction <ul><li>A lot of micro and value chain analysis of livestock in Ethiopia, but little macro-analysis </li></ul><ul><li>Macro-analysis obviously important for several reasons </li></ul><ul><li>Livestock “revolutions” often thought to be demand-led, not supply led (Delgado) </li></ul><ul><li>In Ethiopia, macro issues important: some dynamism in trade, lots of dynamism in prices related to inflation, seasonality, shocks, etc. </li></ul><ul><li>Micro work on improving supply can be “tested” at macro level; e.g. macro impact of improved technologies </li></ul>
    4. 4. Introduction <ul><li>So this study is multifaceted. At the moment we have completed a demand analysis, started a supply/price analysis, and gradually starting CGE and GIS analyses. </li></ul><ul><li>Today we’ll mostly talk about supply/price analysis and demand analysis </li></ul>
    5. 5. Need for better macro-understanding <ul><li>A lot of micro and value chain analysis of livestock in Ethiopia, but little macro-analysis </li></ul><ul><li>Macro-analysis obviously important for several reasons </li></ul><ul><li>Livestock “revolutions” often thought to be demand-led, not supply led (Delgado) </li></ul><ul><li>In Ethiopia, macro issues important: some dynamism in trade, lots of dynamism in prices related to inflation, seasonality, shocks, etc. </li></ul><ul><li>Micro work on improving supply can be “tested” at macro level; e.g. macro impact of improved technologies </li></ul>
    6. 6. Macro-picture and link with policy <ul><li>Construct a macro-picture of the livestock sector </li></ul><ul><li>Understand more about long term constraints and how policy can foster development of livestock sector </li></ul><ul><li>Understand more about shocks and potential for policy response </li></ul>
    7. 7. Figure x. Overview of the research components and their linkages with each other Household demand analysis of livestock General equilibrium model Analysis of livestock prices & values chains GIS (spatial) analysis of livestock sector Integration of markets; effect of shocks on prices; modeling scenarios Patterns of production and market access Spatial data on rainfall & forage shocks Elasticities of own price, cross-price & income demand Partial equilibrium model of livestock dynamics Improved herd dynamics
    8. 8. Market analysis – supply constraints <ul><li>Goal here is to understand how the various livestock markets and production systems work and don’t work, and how prices are formed. </li></ul><ul><li>In terms of understanding of production and trade, we are reviewing the existing literature, including a lot of ILRI work (e.g. IPMS) </li></ul><ul><li>Important for understanding price formation, but also for modelling productivity shocks </li></ul><ul><li>We see differences by sub-sector </li></ul>
    9. 9. An attempt to be diagnostic Beef Shoats Poultry Dairy Comments Feed XXX XXX X XXX Regional & seasonal variation based on farming systems Disease XX XX XXX X Particualrly acute for chickens, but a problem for all sub-sector Marketing, processing, etc XX X X XXX Dairy perishable, so market access critical; export constraints for beef & shoats Breed quality XX XX XX XX Cross-breeding possible over medium/long run Others: labor, credit, extension . . . X X X X Difficult to judge
    10. 10. Market analysis – supply constraints <ul><li>If we roughly agree (?), what are the implications? </li></ul><ul><li>Supply can be expanded with improved health care, breeding, marketing, etc, but feed constraints provide a key linkage to grain sector </li></ul><ul><li>CGE could be useful here; e.g. model the effect of land-intensive vs land-extensive growth in crop production </li></ul>
    11. 11. Market analysis - prices <ul><li>How are livestock prices formed? </li></ul><ul><li>Important for understanding production dynamics, supply response, impacts on consumers, etc </li></ul><ul><li>Two issues of interest </li></ul><ul><li>Integration of markets – Hypothesis – markets are poorly integrated because of high transport costs. There is also product differentiation (different breeds). </li></ul>
    12. 12. Price formation situation <ul><li>2. Price formation, including integration with international markets </li></ul><ul><li>Country has seen sharp rise in meat prices in recent months. </li></ul><ul><li>Hard to see this as domestic demand story since price rise is too big </li></ul><ul><li>If was a general inflation story then other prices should rise (e.g. staples), but not really happening either </li></ul><ul><li>Hypothesis: sharp rise in international meat prices is being transmitted into Ethiopian markets </li></ul><ul><li>So what is the trade story? </li></ul>
    13. 13. Changing composition of livestock exports – fall in hides, big rise in live bovine
    14. 14. Big rise in live animal export values, almost certainly related to increase international prices
    15. 15. But note the changing composition of trade routes and end markets: Djibouti down and Somalia and Sudan up (formal trade replacing informal trade?). UAE way, way up!!
    16. 16. Price formation <ul><li>Research Objectives </li></ul><ul><li>Assess if and how regional and central livestock markets are integrated and </li></ul><ul><li>Identify factors that affect livestock pricing, study their effect on livestock prices, and possibly their effect on trade routes </li></ul><ul><li>Proposed Methods of study: </li></ul><ul><li>1. Integration of markets is to be studied using threshold autoregression (TAR) model and </li></ul><ul><li>2. Gathering data of factors related with price formation and econometric model yet to be determined. </li></ul>
    17. 17. Objectives and a Brief Description of Proposed Study…contd. <ul><li>Basic idea behind TAR: </li></ul><ul><ul><li>To investigate if and how prices markets co-move and </li></ul></ul><ul><ul><li>how price shocks are transmitted </li></ul></ul><ul><li>Differs from simple correlation as it acknowledges there exist thresholds created by transaction costs, </li></ul><ul><li>Simple representation: , where </li></ul><ul><li>and is a white-noise error term. </li></ul><ul><li>where c is the threshold value. </li></ul><ul><li>With: when holds </li></ul>
    18. 18. Data Description <ul><li>Market integration: </li></ul><ul><ul><li>CSA price data spanning July 2001-January 2011, for 8 categories of animals and 119 markets. </li></ul></ul>
    19. 19. Data Description … contd.
    20. 20. Data Description … contd. <ul><li>On factors affecting price formation monthly data collected on: </li></ul><ul><ul><li>International livestock, meat, and diary prices and indices, </li></ul></ul><ul><ul><li>Livestock and meat and meat products exports, </li></ul></ul><ul><ul><li>World crude petroleum and local exchange rate. </li></ul></ul><ul><li>Nominal livestock prices have strong correlation with all variables. </li></ul>
    21. 21. Data Description … contd.
    22. 22. Data Description … contd.
    23. 23. Observations: <ul><li>Some markets seem to be integrated with local markets others with neighboring countries. </li></ul><ul><li>Local livestock prices seem to co-move with international livestock, meat, and dairy prices, </li></ul><ul><li>Other factors also affect the price formation in local markets. </li></ul><ul><li>Comments on content so far welcome, on relevant data and data sources are even more welcome. </li></ul><ul><li>THANK YOU </li></ul>
    24. 24. <ul><ul><li>Nationally representative demand elasticity estimates for livestock products in Ethiopia are unavailable. </li></ul></ul><ul><ul><li>Informs policy design and implementation, welfare analysis and CGE analysis </li></ul></ul>Introduction: Why Demand Elasticities?
    25. 25. <ul><li>Livestock products account for 4.4% of total household expenditure and 8.7% of food expenditure. </li></ul><ul><ul><li>Rural areas: 4.3% of total expenditure and 8% of food expenditure; Urban areas: 5.2% of total expenditure and 12.7% of food expenditure. </li></ul></ul><ul><ul><li>Livestock Expenditure Share of Livestock Products (%) </li></ul></ul><ul><li>Source: Authors’ calculation using HICES (2004/05) </li></ul>Consumption Patterns Beef Mutton & goat meat Chicken Other meat (camel, pork, crocodile...) Fish & fish products Dairy products Egg Honey National 40.9 8.3 7.3 0.5 0.4 39.5 2.6 0.8 Urban 55.8 11.7 9.3 0.5 0.3 17.4 4.7 0.7 Rural 37.9 7.6 6.9 0.5 0.5 44.1 2.2 0.9 Exp. Quintiles Q1 43.0 7.4 7.8 0.5 0.5 38.5 2.0 0.7 Q2 39.9 7.5 7.1 0.5 0.4 42.1 2.0 0.8 Q3 41.0 7.2 7.4 0.5 0.4 41.0 2.2 0.7 Q4 40.5 8.8 7.4 0.4 0.5 39.2 2.7 0.9 Q5 40.4 10.3 6.8 0.4 0.4 37.1 4.0 1.0
    26. 26. <ul><li>Expenditure on meat represents the largest expenditure group followed by dairy products </li></ul><ul><ul><li>Shares of meat, chicken, egg are higher in urban areas while that of dairy products is higher in rural areas </li></ul></ul><ul><li>The Expenditure share of beef & dairy products falls with income/ expenditure. </li></ul><ul><li>Conversely, the share of mutton & goat meat and egg appear to rise with income. </li></ul><ul><li>The expenditure shares of fish & fish products, other meat are negligible. </li></ul>Consumption Patterns (cont’d)
    27. 27. <ul><li>Per Capita Quantity Consumed (kg) </li></ul><ul><li>Source: Authors’ calculation using HICES (2004/05) </li></ul><ul><li>Annual per capita meat consumption in Ethiopia is very low. </li></ul><ul><ul><li>The African and East African average for the same period (2004) stand at about 15 kg and 10 kg respectively (Ethiopian is 8 kg) (FAO, 2010 ). </li></ul></ul>Consumption Patterns (cont’d) Beef Mutton & goat meat Chicken Other meat (camel, pork, crocodile...) Fish & fish products Dairy products Egg Honey Total Meat National 2.6 1.3 0.6 0.1 0.1 13.2 0.2 0.1 3.9 Urban 5.8 2.7 1.4 0.1 0.1 7.2 0.5 0.1 8.5 Rural 2.0 1.0 0.5 0.1 0.1 14.4 0.1 0.1 3.0 Exp. Quintiles Q1 1.3 0.6 0.3 0.1 0.1 7.7 0.1 0.1 1.9 Q2 1.7 0.8 0.4 0.1 0.1 11.6 0.1 0.1 2.5 Q3 2.2 1.0 0.5 0.1 0.1 13.0 0.1 0.1 3.2 Q4 2.9 1.4 0.7 0.1 0.1 15.6 0.2 0.1 4.3 Q5 4.6 2.4 1.2 0.1 0.1 17.2 0.5 0.2 7.0
    28. 28. <ul><ul><li>Individuals in urban areas are likely to consume about three times as much as their rural counterparts. </li></ul></ul><ul><ul><li>People in the richest quintile consume over three times more than those in the poorest quintile. </li></ul></ul><ul><li>Urban areas also have substantially higher consumption of chicken and eggs </li></ul><ul><li>Dairy products represent the biggest consumption group. </li></ul><ul><ul><li>The quantity consumed is considerably higher in rural areas than in urban areas </li></ul></ul><ul><ul><li>Though its share appears to fall with income, the level (quantity) of consumption rather rises with income. </li></ul></ul>Consumption Patterns (cont’d)
    29. 29. <ul><li>Per capita calorie intake </li></ul><ul><ul><li>Source: Authors’ calculation using HICES (2004/05) </li></ul></ul><ul><li>Livestock products contribute a very small share of daily calorie intake in Ethiopia (only 2.4%). </li></ul><ul><ul><li>It appears urban households get more energy from livestock products than rural ones (shares – 4.2% Vs 2.1%) </li></ul></ul>Consumption Patterns (cont’d) Beef Mutton & goat meat Chicken Other meat (camel, pork, crocodile...) Fish & fish products Dairy products Egg Honey Total National 14.5 7.4 3.1 0.5 0.4 27.6 1.0 0.7 55.2 Urban 33.8 16.7 7.4 0.6 0.3 20.0 3.1 1.0 82.9 Rural 10.6 5.6 2.2 0.4 0.4 29.1 0.6 0.6 49.5 Exp. Quintiles Q1 7.1 3.3 1.4 0.4 0.1 14.3 0.2 0.3 27.1 Q2 9.1 4.8 1.8 0.5 0.5 24.9 0.3 0.5 42.4 Q3 11.8 5.8 2.4 0.4 0.1 24.2 0.6 0.5 45.8 Q4 15.8 7.8 3.2 0.5 0.5 33.0 0.8 0.6 62.2 Q5 27.2 14.6 6.1 0.5 0.4 39.2 2.7 1.3 92.0
    30. 30. <ul><ul><li>People in the fifth exp. quintile get three times more calorie than those in the first quintile from livestock products. </li></ul></ul><ul><li>For all livestock products, per capita calorie intake rises with income. </li></ul><ul><li>Among livestock products, dairy products are the most important source of calorie (50%) followed by meat. </li></ul><ul><ul><li>Rural households get more calories from dairy products than urban ones (share - 59% Vs 24%). </li></ul></ul><ul><ul><li>The richest 20% percent of households get three times more calorie from dairy products than their poorest counterparts. </li></ul></ul><ul><li>People in urban areas get three times more calorie from meat (beef, mutton & goat meat) than those in rural areas. </li></ul><ul><ul><li>People in the richest quintile get four times more calorie from meat than their those in the poorest quintile. </li></ul></ul>Consumption Patterns (cont’d)
    31. 31. <ul><li>Data </li></ul><ul><ul><li>Nationally representative survey datasets for 2004/05 – Household Income, Consumption, and Expenditure Survey (HICES) and Welfare Monitoring Survey (WMS) </li></ul></ul><ul><ul><li>No price data, unit values proved to be problematic. So external price data from CSA’s price survey used. </li></ul></ul><ul><ul><li>Caveat </li></ul></ul><ul><ul><ul><ul><li>The data used in this study excludes all zones of the Gambella region, and three predominantly non-sedentary zones of Afar region and six such zones of Somali region </li></ul></ul></ul></ul>Estimating elasticities - Methodology – Data
    32. 32. <ul><li>Expenditure Elasticities </li></ul><ul><li>Standard Errors in Brackets; ***,**,* are significance level at 1%, 5% & 10% </li></ul><ul><li>All livestock products have positive and significant expenditure elasticity </li></ul><ul><ul><li>Normal goods! </li></ul></ul><ul><li>The expenditure elasticity of beef is close to unity and is the highest, while that of dairy products is the lowest. </li></ul><ul><ul><li>Beef appears superior to all other livestock products. </li></ul></ul>Results Total Urban Rural Beef 0.939*** 0.896*** 0.985*** [0.0178] [0.0198] [0.0423] Mutton & Goat meat 0.671*** 0.304*** 0.917*** [0.1268] [0.1138] [0.1361] Other meat & animal products 0.538*** 0.519*** 1.045*** [0.0455] [0.0551] [0.0757] Dairy products 0.420*** 0.389*** 0.479*** [0.0148] [0.0136] [0.0061]
    33. 33. <ul><li>Expenditure elasticities appear to be higher in rural areas than in urban areas. </li></ul><ul><ul><li>The gap between the two sets of elasticities is particularly high for beef, mutton & goat meat, and other meat & animal products </li></ul></ul><ul><ul><ul><li>Urban areas have higher budget shares for these commodities </li></ul></ul></ul><ul><ul><li>Dairy products: reluctance to change consumption habit… </li></ul></ul>Results (cont’d)
    34. 34. Results (cont’d) – Compensated own-Price and cross-price Elasticities Beef Mutton & Goat meat Other meat & animal products Dairy products Beef Total -0.733*** 0.391*** 0.083*** 0.259*** [0.0233] [0.0193] [0.0073] [0.0105] Urban -0.665*** 0.382*** 0.136*** 0.147*** [0.0235] [0.0176] [0.0071] [0.0087] Rural -0.793*** 0.378*** 0.031 0.386*** [0.0786] [0.0577] [0.0266] [0.0331] Mutton & Goat meat Total 1.554*** -1.465*** 0.069 -0.158* [0.1003] [0.1144] [0.0426] [0.0846] Urban 1.889*** -1.882*** 0.184*** -0.192*** [0.1103] [0.1144] [0.0378] [0.0613] Rural 2.822*** -2.008*** -0.292 -0.523** [0.6603] [0.4221] [0.2907] [0.2565] Other meat & animal products Total 0.403*** 0.395*** -0.996*** 0.194*** [0.0285] [0.0337] [0.0229] [0.0336] Urban 0.71*** 0.181*** -0.988*** 0.071** [0.0476] [0.0386] [0.0228] [0.0289] Rural -0.056 0.289 -1.039*** 0.806*** [0.233] [0.1926] [0.0698] [0.0989] Dairy products Total 0.405*** -0.249*** 0.28*** -0.669*** [0.0244] [0.0344] [0.0152] [0.0344] Urban 0.06 0.062 0.304*** -0.523** [0.098] [0.0868] [0.0289] [0.2613] Rural 0.134 -0.009 0.247*** -0.151** [0.1256] [0.0618] [0.0577] [0.0746]
    35. 35. <ul><li>All own price effects are negative and significant. </li></ul><ul><ul><li>Mutton/goat meat has the highest own price elasticity </li></ul></ul><ul><li>For beef, mutton/goat meat, and ‘other meat’, rural areas have higher own elasticities, while the opposite is true for dairy products. </li></ul><ul><ul><li>Remember the budget shares? </li></ul></ul><ul><li>There is a strong substitution relationship between beef and mutton/goat meat. </li></ul><ul><li>Dairy products are substitutes for beef and other meat & animal products, but complements for mutton & goat meat. </li></ul><ul><ul><li>Effect is higher in rural areas. </li></ul></ul>Results (cont’d)
    36. 36. <ul><ul><li>Nationally representative demand elasticity estimates for livestock products in Ethiopia are unavailable. </li></ul></ul><ul><ul><li>Informs policy design and implementation. </li></ul></ul><ul><ul><ul><li>Useful for Welfare analysis </li></ul></ul></ul><ul><ul><ul><li>Useful for CGE analysis </li></ul></ul></ul>Introduction: Why Demand Elasticities?
    37. 37. <ul><li>Livestock products account for 4.4% of total household expenditure and 8.7% of food expenditure. </li></ul><ul><ul><li>Rural areas: 4.3% of total expenditure and 8% of food expenditure; Urban areas: 5.2% of total expenditure and 12.7% of food expenditure. </li></ul></ul><ul><ul><li>Livestock Expenditure Share of Livestock Products (%) </li></ul></ul><ul><li>Source: Authors’ calculation using HICES (2004/05) </li></ul>Consumption Patterns Beef Mutton & goat meat Chicken Other meat (camel, pork, crocodile...) Fish & fish products Dairy products Egg Honey National 40.9 8.3 7.3 0.5 0.4 39.5 2.6 0.8 Urban 55.8 11.7 9.3 0.5 0.3 17.4 4.7 0.7 Rural 37.9 7.6 6.9 0.5 0.5 44.1 2.2 0.9 Exp. Quintiles Q1 43.0 7.4 7.8 0.5 0.5 38.5 2.0 0.7 Q2 39.9 7.5 7.1 0.5 0.4 42.1 2.0 0.8 Q3 41.0 7.2 7.4 0.5 0.4 41.0 2.2 0.7 Q4 40.5 8.8 7.4 0.4 0.5 39.2 2.7 0.9 Q5 40.4 10.3 6.8 0.4 0.4 37.1 4.0 1.0
    38. 38. <ul><li>Expenditure on meat represents the largest expenditure group followed by dairy products </li></ul><ul><ul><li>Shares of meat, chicken, egg are higher in urban areas while that of dairy products is higher in rural areas </li></ul></ul><ul><li>The Expenditure share of beef & dairy products falls with income/ expenditure. </li></ul><ul><li>Conversely, the share of mutton & goat meat and egg appear to rise with income. </li></ul><ul><li>The expenditure shares of fish & fish products, other meat are negligible. </li></ul><ul><ul><li>Fish - The landlocked status of Ethiopia and/or lack of culture of fish consumption could be the reason. </li></ul></ul>Consumption Patterns (cont’d)
    39. 39. <ul><li>Per Capita Quantity Consumed (kg) </li></ul><ul><li>Source: Authors’ calculation using HICES (2004/05) </li></ul><ul><li>Annual per capita meat consumption in Ethiopia is very low. </li></ul><ul><ul><li>The African and East African average for the same period (2004) stand at about 15 kg and 10 kg respectively (Ethiopian is 8 kg) (FAO, 2010 ). </li></ul></ul>Consumption Patterns (cont’d) Beef Mutton & goat meat Chicken Other meat (camel, pork, crocodile...) Fish & fish products Dairy products Egg Honey Total Meat National 3.1 1.4 0.7 0.1 0.1 16.7 0.2 0.1 4.6 Urban 6.8 3.1 1.6 0.1 0.0 8.5 0.5 0.1 9.9 Rural 2.4 1.1 0.5 0.1 0.1 18.4 0.1 0.1 3.5 Exp. Quintiles Q1 1.6 0.7 0.3 0.0 0.0 10.2 0.0 0.0 2.3 Q2 2.1 1.0 0.4 0.1 0.1 15.2 0.1 0.1 3.1 Q3 2.7 1.2 0.6 0.1 0.0 16.9 0.1 0.1 3.9 Q4 3.5 1.6 0.7 0.1 0.1 19.9 0.2 0.1 5.0 Q5 5.3 2.7 1.3 0.1 0.1 20.6 0.5 0.1 8.0
    40. 40. <ul><ul><li>Individuals in urban areas are likely to consume about three times as much as their rural counterparts. </li></ul></ul><ul><ul><li>People in the richest quintile consume three times more than those in the poorest quintile. </li></ul></ul><ul><li>Urban areas also have substantially higher consumption of chicken, egg </li></ul><ul><li>Dairy products represent the biggest consumption group. </li></ul><ul><ul><li>The quantity consumed is considerably higher in rural areas than in areas </li></ul></ul><ul><ul><li>Though its share appears to fall with income, the level (quantity) of consumption rather rises with income. </li></ul></ul>Consumption Patterns (cont’d)
    41. 41. <ul><li>Per capita calorie intake </li></ul><ul><ul><li>Source: Authors’ calculation using HICES (2004/05) </li></ul></ul><ul><li>Livestock products contribute a very small share of daily calorie intake in Ethiopia (only 2.4%). </li></ul><ul><ul><li>It appears urban households get more energy from livestock products than rural ones (shares – 4.2% Vs 2.1%) </li></ul></ul>Consumption Patterns (cont’d) Beef Mutton & goat meat Chicken Other meat (camel, pork, crocodile...) Fish & fish products Dairy products Egg Honey Total National 17.5 8.8 3.6 0.5 0.4 34.6 1.1 0.7 67.3 Urban 39.2 19.3 8.6 0.7 0.3 23.1 3.5 1.2 95.8 Rural 13.1 6.7 2.6 0.5 0.4 36.9 0.6 0.7 61.4 Exp. Quintiles Q1 9.0 4.0 1.6 0.4 0.1 18.9 0.3 0.3 34.7 Q2 11.4 6.0 2.2 0.5 0.6 32.3 0.4 0.7 53.9 Q3 14.8 7.2 2.9 0.5 0.1 31.7 0.6 0.5 58.3 Q4 19.2 9.3 3.8 0.5 0.7 41.4 1.0 0.7 76.7 Q5 31.3 16.6 7.1 0.6 0.4 46.1 3.0 1.4 106.6
    42. 42. <ul><ul><li>People in the fifth exp. quintile get three times more calorie than those in the first quintile from livestock products. </li></ul></ul><ul><li>For all livestock products, per capita calorie intake rises with income. </li></ul><ul><li>Among livestock products, dairy products are the most important source of calorie (51%) followed by meat. </li></ul><ul><ul><li>Rural households get more calories from dairy products than urban ones (share - 60% Vs 24%). </li></ul></ul><ul><ul><li>The richest 20% percent of households get twice more calorie from dairy products than their poorest counterparts. </li></ul></ul><ul><li>People in urban areas get three times more calorie from meat (beef, mutton & goat meat) than those in rural areas. </li></ul><ul><ul><li>People in the richest quintile get about four times more calorie from meat than their those in the poorest quintile. </li></ul></ul>Consumption Patterns (cont’d)
    43. 43. <ul><li>Model - the QU-AIDS (Banks, Blundell and Lewbel (1997)). </li></ul><ul><li>Restrictions </li></ul>Methodology - Model
    44. 44. <ul><ul><li>Zero expenditures/consumption </li></ul></ul><ul><ul><ul><li>Problem : Such censoring can produce biased and inconsistent parameter estimates; </li></ul></ul></ul><ul><ul><ul><li>Solution : two-step estimation (Shonkwiler and Yen (1999)): </li></ul></ul></ul><ul><li>Expenditure endogeneity </li></ul><ul><li>Use the residual from (8) with in the shares regression </li></ul>Methodology - Issues
    45. 45. <ul><li>Data </li></ul><ul><ul><li>Nationally representative survey datasets for 2004/05 – Household Income, Consumption, and Expenditure Survey (HICES) and Welfare Monitoring Survey (WMS) </li></ul></ul><ul><ul><li>No price data, unit values proved to be problematic. So external price data from CSA’s price survey used. </li></ul></ul><ul><ul><li>Caveat </li></ul></ul><ul><ul><ul><ul><li>The data used in this study excludes all zones of the Gambella region, and three predominantly non-sedentary zones of Afar region and six such zones of Somali region </li></ul></ul></ul></ul><ul><li>Estimated Budget Share Equations </li></ul>Methodology – Data; Specification
    46. 46. <ul><li>Expenditure Elasticities </li></ul><ul><li>Standard Errors in Brackets; ***,**,* are significance level at 1%, 5% & 10% </li></ul><ul><li>All livestock products have positive and significant expenditure elasticity </li></ul><ul><ul><li>Normal goods! </li></ul></ul><ul><li>The expenditure elasticity of beef is close to unity and is the highest, while that of dairy products is the lowest. </li></ul><ul><ul><li>Beef appears superior to all other livestock products. </li></ul></ul>Results Total Urban Rural Beef 0.939*** 0.896*** 0.985*** [0.0178] [0.0198] [0.0423] Mutton & Goat meat 0.671*** 0.304*** 0.917*** [0.1268] [0.1138] [0.1361] Other meat & animal products 0.538*** 0.519*** 1.045*** [0.0455] [0.0551] [0.0757] Dairy products 0.420*** 0.389*** 0.479*** [0.0148] [0.0136] [0.0061]
    47. 47. <ul><li>Expenditure elasticities appear to be higher in rural areas than in urban areas. </li></ul><ul><ul><li>The gap between the two sets of elasticities is particularly high for beef, mutton & goat meat, and other meat & animal products </li></ul></ul><ul><ul><ul><li>Urban areas have higher budget shares for these commodities </li></ul></ul></ul><ul><ul><li>Dairy products: reluctance to change consumption habit… </li></ul></ul>Results (cont’d)
    48. 48. <ul><li>Compensated Price Elasticities </li></ul><ul><li>Standard Errors in Brackets; ***,**,* are significance level at 1%, 5% & 10% </li></ul>Results (cont’d) Beef Mutton & Goat meat Other meat & animal products Dairy products Beef Total -0.733*** 0.391*** 0.083*** 0.259*** [0.0233] [0.0193] [0.0073] [0.0105] Urban -0.665*** 0.382*** 0.136*** 0.147*** [0.0235] [0.0176] [0.0071] [0.0087] Rural -0.793*** 0.378*** 0.031 0.386*** [0.0786] [0.0577] [0.0266] [0.0331] Mutton & Goat meat Total 1.554*** -1.465*** 0.069 -0.158* [0.1003] [0.1144] [0.0426] [0.0846] Urban 1.889*** -1.882*** 0.184*** -0.192*** [0.1103] [0.1144] [0.0378] [0.0613] Rural 2.822*** -2.008*** -0.292 -0.523** [0.6603] [0.4221] [0.2907] [0.2565] Other meat & animal products Total 0.403*** 0.395*** -0.996*** 0.194*** [0.0285] [0.0337] [0.0229] [0.0336] Urban 0.71*** 0.181*** -0.988*** 0.071** [0.0476] [0.0386] [0.0228] [0.0289] Rural -0.056 0.289 -1.039*** 0.806*** [0.233] [0.1926] [0.0698] [0.0989] Dairy products Total 0.405*** -0.249*** 0.28*** -0.669*** [0.0244] [0.0344] [0.0152] [0.0344] Urban 0.06 0.062 0.304*** -0.523** [0.098] [0.0868] [0.0289] [0.2613] Rural 0.134 -0.009 0.247*** -0.151** [0.1256] [0.0618] [0.0577] [0.0746]
    49. 49. <ul><li>All own price effects are negative and significant. </li></ul><ul><ul><li>Mutton/goat meat has the highest own price elasticity </li></ul></ul><ul><li>For beef, mutton/goat meat, and ‘other meat’, rural areas have higher own elasticities, while the opposite is true for dairy products. </li></ul><ul><ul><li>Remember the budget shares? </li></ul></ul><ul><li>There is a strong substitution relationship between beef and mutton/goat meat. </li></ul><ul><li>Dairy products are substitutes for beef and other meat & animal products, but complements for mutton & goat meat. </li></ul><ul><ul><li>Effect is higher in rural areas. </li></ul></ul>Results (cont’d)

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