Impact of Food Price Increase on Poverty and Policy Responses Preliminary Findings from West & Central Africa Quentin Wodon with core team comprising of Franck Adoho, Prospere Backiny Yetna, Harold Coulombe, George Joseph, Juan Carlos Parra Osorio, and Clarence Tsimpo World Bank, BBL June 3, 2008
Objectives of Presentation Suggesting how surveys & SAMs can be used for analysis on food price increase & policy response. 4 parts in presentation: 1. Impact on poverty of increase in food prices 2. Analysis of geography of impacts 3. SAM-based analysis: multiplier effects 4. Incidence analysis of policy responses: indirect tax reforms; public works; targeted food aid [Note: Results are preliminary]
Part 1 Impact of Higher Food Prices Methodology Household survey data 1 to 8 items per country; Selected items account for 6.5% of total consumption in Togo to 28.3% in DRC and 41.0% in Niger. In most countries, share of food items considered <15% of cons. Net purchases of food, Net sales of food, Auto-consumption Assumptions Increase in net purchases/sales as impact on consumption No spillover effects on other food items Same percentage increase in consumer & producer prices Arbitrary increases in prices: 12.5%, 25%, 50%, 100% Interpretation Consumer side impact: Upper bound impact Combined consumer/producer impact: Lower bound impact (not clear that price increases will trickle down to producers due to intermediaries, and possibility of increases in production costs)
Net sales & net purchases Illustration for Ghana Rice Maize 0.9 1 Net Consumer 0.8 0.9 0.7 0.8 0.6 0.5 0.7 0.6 Autarky Fraction 0.4 Fraction 0.5 0.4 0.3 0.2 Autarky 0.3 Net Consumer 0.2 0.1 Net Producer 0.1 Net Producer 0 10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5-0.1 Log of per eq adult expenditure Net Producer Net Consumer Autarky 0 10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 Log of per eq adult expenditure Net Producer Net Consumer Autarky
Food Items Included in Analysis Food items considered (first 12 countries another 6 in progress) Country Survey Food Items Taken into account for simulations Burkina Faso QUIBB, 2003 Rice, Bread, Vegetable oil and butter, Sugar, Milk Dem. Rep. Congo 123 Survey, 2005 Rice, Cassava, Maize, Palm oil, Plantain, Wheat, Sugar, Milk Ghana GLSS, 2005-06 Rice, Bread, Flour, Maize Gabon CWIQ, 2005 Rice, Cassava, Maize, Wheat, Palm oil and groundnut oil Guinea EIBEP, 2002-03 Rice Liberia CWIQ, 2007 Rice (locally produced and imported) Mali ELIM, 2006 Rice, Millet, Maize, Wheat Niger QUIBB, 2005 Rice (locally produced and imported), Millet, Sorghum Nigeria NLSS, 2003-04 Rice, Corn, Maize, Wheat flour and bread, Cassava Senegal ESPS, 2006 Rice, Vegetable oil, Sugar, Bread, Milk Sierra Leone SLLS, 2003 Rice Togo QUIBB, 2006 Rice, Vegetable oil, Sugar, Bread, Milk
Estimates of Impact of Higher Prices Percentage Point Increase in Poverty 9 8 7 6 5 4 3 2 1 Figure 1: Upper and Lower Bound Poverty Impacts Upper Bound Lower Bound 0 Ghana Togo Guinee Nigeria Sierra Leone Gabon RDC Mali Liberia Niger
Comparisons of Impacts National estimates (headcount, 50 percent increase in prices): Upper bound: 4.4 points Lower bound: 2.5 points Urban areas Upper bound: 5.2 points Lower bound: 3.7 points Rural areas Upper bound: 4.1 points Lower bound: 2.3 points Comments An average 3.5 point impact at the national level for all of sub-saharan Africa, which has a population of more than 800 million, would lead to an increase in population in poverty of close to 30 million persons. In addition, all households who are already in poverty would be poorer as well and (poverty gap increase in proportional terms is typically larger Limited ability of many households to raise cash needed to buy food (in the analysis, we compare increase in prices to consumption, not income)
Comparisons of Impacts Differences between upper & lower bounds Differences are smallest for Niger, Liberia, and Gabon, 3 countries with large net imports of food (Senegal similar but not shown on Figure because we do not have lower bound estimates). In addition, in Liberia and Niger local food production is important bust mostly auto-consumed (and thus not taken into account in simulations) Differences between urban and rural impacts In most countries, urban impact is higher, but not everywhere In Ghana, poverty is low in urban areas so that only a small percentage of urban dwellers fall into poverty (ability to cope). In Senegal and Liberia, much of food consumption is imported. Hence even the rural poor suffer a lot from the price shocks. When data are available for the capital city separately from other urban areas (e.g., in Senegal and Togo), we find that impacts are largest in urban areas outside of the capital city
Comparisons of impacts for rice 7 Figure 2: Upper Bound Estimates for Impact of Increase in Price of Rice Percentage Point Increase in Poverty 6 5 4 3 2 1 25% increase 50% increase 0 Gabon Ghana RDC Burkina Nigeria Togo Niger Sierra Leone Mali Guinee Senegal Liberia
Part 2 Geography of impacts Background: 16 country poverty mapping project Estimation of poverty map before and after shock (using consumption aggregate from the survey before and after the shock, same census data) Same assumptions as for survey-based work Upper and lower bounds Selection of number of food items Arbitrary increase in food prices Key issue: poorest areas are often not hardest hit dilemma for policy (responding to shock or keeping focus on the very poor; political economy)
Geography of impacts: Ghana example Figure 1: Ghana Poverty Map and Impact of 50 Percent Price Increase for Five Food Items (A) Poverty Map for 2006 (B) Upper Bound Poverty Impact Source: Authors estimation using 2003 CWIQ and 2005/06 GLSS5 data.
Geography of impacts: Policy dilema Figure 2: Change in Poverty and Initial Poverty in four West African Countries (A) Ghana (B) Guinea 0.0 1.0 2.0 3.0 4.0 Change in Poverty Headcount 0.0 2.0 4.0 6.0 8.0 Change in Poverty Headcount 0.0 20.0 40.0 60.0 80.0 Poverty Headcount 20.0 40.0 60.0 80.0 100.0 Poverty Headcount (C) Niger (D) Senegal 0.0 5.0 10.0 15.0 Change in Poverty Headcount 0.0 5.0 10.0 15.0 Change in Poverty Headcount 20.0 40.0 60.0 80.0 100.0 Poverty Headcount 20.0 40.0 60.0 80.0 100.0 Poverty Headcount
Part 3 Multiplier effects Using SAMs Methodology: SAM as price model Trade multiplier (only exogenous account is ROW) No behavioral response quantities proportions used as intermediate consumption remain constant Comparison of oil and imported food price shocks 25% price increase for both and assumption that food price shocks affect the activity as a whole and not only imports (note model is linear, hence 25% does not matter) Interest in direct vs. indirect effects Estimations to be done for a dozen countries (8 countries done so far: Burkina Faso, Burundi, Ghana, Guinea, Kenya, Mali, Niger, Senegal)
Multipliers Ghana example Rice accounts for 4.1% ot household consumption vs. 2.1% for oil Cost of living impact of oil price shock is 4.1% vs. 2.2% for rice Indirect effects are much larger for oil than for rice (conversely, direct effect is 12% of total effect for oil, versus 42% for rice) Category Change in cost of living (1) Direct effect (2) Direct effect as share of total effect (2)/(1) Share of good in final household consumption Share of aggregate households expenditure Oil price shock Rural 4.26 0.74 17.26 3.20 48.11 Urban 3.96 0.26 6.67 1.14 51.89 Total (CPI) 4.11 0.49 11.96 2.13 Rice price shock Rural 2.25 0.93 41.20 4.03 48.11 Urban 2.20 0.94 42.93 4.08 51.89 Total (CPI) 2.22 0.93 42.09 4.06 Note: Direct effect is product of three shares: (a) share of total consumption accounted for product; (b) share of total income allocated to consumption; and (c) price increase for the good
Multipliers Summary Findings Percentage change in households cost of living due to a 25% price shock Oil Food Change in cost of Share in final Total supply as Change in cost of Total supply as % of GDP Country (year SAM food Share in final item) living consumption % of GDP living consumption Burkina Faso (04 - Rice) 5.27 2.08 5.74 1.42 1.26 1.35 Burundi (04 Other export crops) 0.58 0.38 0.20 2.18 2.40 4.94 Ghana (05 - Rice) 4.11 2.13 10.13 2.22 4.06 4.21 Guinea (05 - Food) 8.02 8.78 9.73 12.49 27.49 20.37 Kenya (01 - Maize) 1.78 2.69 22.24 3.85 9.76 7.75 Mali (04 - Grains) 2.76 4.01 11.72 1.74 5.94 3.94 Niger (04 Food agriculture) Senegal (04 Grains and cereals) 2.98 4.38 4.95 5.79 13.98 10.90 2.81 0.76 13.34 7.36 12.20 26.76
Part 4 Policy: (1) Indirect Tax Cut Several variables influence policy makers when considering tax cuts: (1) share in total consumption represented by items. A higher share means more pressure to reduce taxes on goods in a time of crisis. (2) share of the population likely to be affected by the price shock. This share maters from a political economy point of view. (3) What matters more for poverty reduction though is the share of a good s consumption accounted for by the poor in the population. Analysis Focus on imported foods (where import tax/vat can be reduced) For comparability: poor are bottom 40% or bottom 60% of pop. Robustness tests Consumption Dominance Curves
Indirect Tax Cut Data Table 1: Basic Statistics and Benefit Incidence of Reduction in Indirect Taxes on Imported Food Food item Share in Proportion Share consumed Share consumed Total consumption Consumers by bottom 40% by bottom 60% Burkina Faso (2003 survey); Base Share in Poverty at 46.4% Rice 3.6 60.2 13.4 25.6 Bread 0.7 35.6 8.3 18.1 Vegetable oil, butter 1.1 74.9 16.1 31.6 Sugar 0.9 67.4 19.7 35.3 Milk 0.6 18.1 10.3 19.8 Democratic Republic of Congo (2005 survey); Base Share in Poverty at 71.3% Rice 3.2 57.3 15.5 31.7 Palm oil 4.0 96.2 19.7 36.2 Wheat 1.8 35.1 7.1 17.4 Sugar 1.4 57.4 10.6 24.6 Milk 0.7 23.0 4.1 11.6 Gabon (2005 survey); Base Share in Poverty at 32.7% Rice 3.0 91.4 31.7 51.1 Maize 0.3 40.0 14.9 31.7 Wheat 3.9 93.5 27.9 46.8 Palm oil and groundnut oil 1.7 90.6 30.1 48.6 Ghana (2006 survey) ; Base Share in Poverty at 28.5% Rice 3.1 74.6 16.4 33.0 Bread 1.9 84.6 14.2 29.5 Flour 0.0 2.8 45.0 60.4
Indirect Tax Cut Data Table 1: Basic Statistics and Benefit Incidence of Reduction in Indirect Taxes on Imported Food Food item Share in Proportion Share consumed Share consumed Total consumption Consumers by bottom 40% by bottom 60% Guinea (2003 survey) ; Base Share in Poverty at 49.1% Rice 13.0 90.7 23.1 42.8 Liberia (2007 survey) ; Base Share in Poverty at 63.8% Local Rice 9.6 60.1 27.5 47.8 Imported Rice 13.2 84.9 22.3 41.2 Total Rice 22.8 99.0 24.5 44.0 Mali (2006 survey) ; Base Share in Poverty at 47.5% Rice 7.2 95.1 11.1 25.1 Corn 4.2 91.0 14.4 33.1 Wheat 1.5 74.0 19.5 36.7 Niger (2005 survey) ; Base Share in Poverty at 62.1% Rice Imported 4.4 54.7 14.8 31.4 Rice local 1.7 15.4 20.1 35.9 Maize 4.3 30.4 18.2 34.3 Senegal (2006 survey) ; Base Share in Poverty at 50.8% Rice 6.8 96.3 28.0 47.9 Vegetable oil 4.5 95.8 22.8 42.1 Sucre 3.0 99.2 27.1 46.6 Bread 4.0 92.7 14.8 32.6 Milk 2.1 79.6 10.0 23.4
Indirect Tax Cut Data Table 1: Basic Statistics and Benefit Incidence of Reduction in Indirect Taxes on Imported Food Food item Share in Proportion Share consumed Share consumed Total consumption Consumers by bottom 40% by bottom 60% Sierra Leone (2003 survey) ; Base Share in Poverty at 66.4% Rice 11.7 96.4 32.0 53.9 Togo (2006 survey) ; Base Share in Poverty at 61.6% Rice 3.5 92.2 23.0 40.4 Bread 0.6 27.0 5.8 15.5 Milk 0.7 31.1 7.6 18.4 Vegetable oil 1.1 81.3 21.3 39.5 Sugar 0.7 72.3 20.1 36.7 Nigeria (2004 survey); Base Share in Poverty at 54.7% Rice 4.1 73.4 14.0 30.2 Wheat flour and bread 1.5 70.4 12.5 27.0 Source: Authors estimation using respective household surveys.
Robustness test: CD curves 1 0.9 0.8 Palm Oil 0.7 0.6 Rice 0.5 Total foods 0.4 0.3 Smoked Fish Fesh Fish 0.2 Chicken Bread & Buns 0.1 Sugar 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 Total per Eq. Adult Consumption/Z Rice Palm Oil Fresh Fish Chicken Smoked Fish Sugar Bread and Buns Total foods
Summary on Indirect Tax Cut Share of food consumption in the bottom 40 percent: For rice, this share varies from 11.1 percent in Mali to 32.0 percent in Sierra Leone. Thus, if we consider the bottom 40 percent as the poor, out of every dollar spent by a government for reducing indirect taxes on rice, only about 20 cents on average will benefit the poor. This is a rather low proportion, and it assumes that tax reductions do trickle down to lower prices for consumers, which is not obvious. If we consider that the bottom 60 percent of the population can be considered as poor, the share varies from 25.1 percent to 53.9 percent, which still does not suggest good targeting. For some other goods imported, the proportions are even lower BUT there may be spillover effects example of imported rice and locally produced rice potentially larger impact on consumers (but then one must also consider the potential impact on producers)
Part 4 Policy: (2) Public Works Labor intensive public works often presented as good alternative, but (1) In African context where many workers work without pay or at very low pay, targeting performance is not guaranteed (2) If program participants must give up other income sources to participate, there may be large substitution effects (3) Public works have potentially substantial adminsitrative costs as well as other costs (e.g., for building materials) so that only part of total costs turn out to benefit households in the short term Analysis Identify potential beneficiaries through employment and wage data Measure leakage and substitution effects using same data Assess impacts on poverty using simplifying assumptions
Number of potential beneficiaries: Ghana Comparison of potential beneficiaries at various wage levels Urban Areas Rural Areas Figure 2: Distribution of potential beneficiaries of public works, Urban, no education criteria and low wages Figure 3: Distribution of potential beneficiaries of public works, Rural, no education criteria and low wages Number of Beneficiaries, in '000 0 50 100 150 200 250 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 E150 E200 E250 E300 Number of Beneficiaries, in '000 0 200 400 600 800 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 E150 E200 E250 E300 Source: Authors estimation using GLSS5 data. Source: Authors estimation using GLSS5 data.
Number of potential beneficiaries: Liberia Comparison of potential beneficiaries at various wage levels Figure 1: Distribution of potential beneficiaries of public works 20-40, 2007 Number of Beneficiaries, in 000 0 20 40 60 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5 E10000 E15000 E20000 E25000 Number of Beneficiaries, in '000 0 20 40 60 80 100 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5 E10000 E15000 E20000 E25000 Urban areas Rural areas
Leakage Rates - Ghana Leakage rate very high in Ghana (>70%) if no geographic targeting Most of the leakage rate comes from non-poor participants pay Wage substitution effects are smaller (but depends on assumptions) Table 7: Potential leakage effects of public works for poverty reduction, by region without education criteria and low wages, 2005-2006 Region #of Poverty Additional Leakage people Headcount Wage Rate in% In'000monthly in% Total 2,481,780 34.6 160.8 72.2 E250 Western 193,442 20.0 199.1 84.1 Central 137,462 17.2 190.8 86.9 Greater Accra 155,500 7.0 172.7 95.2 Volta 200,906 35.8 190.2 72.7 Eastern 241,958 18.5 187.8 86.1 Ashanti 409,276 22.0 195.9 82.8 Brong Ahafo 271,192 33.6 210.9 71.6 Northern 434,156 54.1 200.7 56.6 Upper East 277,412 69.7 214.6 40.2 Upper West 348,320 83.9 244.5 17.9 Total 2,669,624 33.7 198.7 73.2
Leakage Rates - Liberia Comparison of leakage rates for poverty and extreme poverty Region Table 1: Leakage rate for public works by region, 2007 Poverty Leakage Rate in% Ext. Pov. Leakage Rate in% Poverty Leakage Rate in% Ext. Pov. Leakage Rate in% E10000 Greater Monrovia 63.7 82.3 E20000 59.0 79.8 North Central 49.0 59.2 49.6 58.4 North Western 53.6 66.8 52.3 62.0 South Central 48.6 67.1 51.0 65.3 South Eastern A 36.3 55.8 47.2 57.3 South Eastern B 45.7 64.3 50.5 61.9 Total 50.0 64.2 51.3 63.2 E15000 Greater Monrovia 60.8 81.1 E20000t 58.7 80.2 North Central 51.5 59.9 47.1 56.3 North Western 53.1 62.2 47.7 58.1 South Central 52.0 65.7 50.2 65.0 South Eastern A 45.5 58.0 43.9 55.3 South Eastern B 52.8 63.8 48.7 60.4 Total 52.4 63.7 49.4 62.1 Source: Wodon, Tsimpo and Graham (2008), based on 2007 CWIQ survey.
Part 4 Policy: (3) Targeted Aid Few surveys have detailed information, but some have (1) Liberia Who benefits from various types of food distributions (no amount available however) (2) Burundi Consumption module distinguishes between four types of consumption: purchases of food, auto-consumption of food; food received from friends and relatives (private transfers); and food received from NGOs or government (which matches WFP amounts) Analysis Targeting performance (only summary discussed here; but mention of availability of new richer incidence analysis framework) Impact of aid on monetary poverty and food security (as measured through kcal intake per equivalent adult) using matching techniques
Incidence Analysis Framework (Choice of indicator) Question: Who benefits from aid/subsidies? Parameter: Ω = share of subsidies for a service/good received by the poor divided by share of poor in population Example: if share of population in poverty is 54.7 in Nigeria, and the poor get 27.3 of a subsidy, then Ω=0.5 Objective for targeting: Ω as large as possible (if Ω >1, subsidies considered as pro-poor ) Why care about targeting? Lower subsidy budget More bang for the buck in terms of poverty reduction Fewer distortions in consumption decisions
Incidence Analysis Framework (What drives targeting performance?) Beneficiary targeting: Share of beneficiaries of subsidies (e.g., for education, health, etc.) who are poor. 3 factors: Supply of service (there may be limited geographic coverage of service providers distance to facilities) Take-up by household (there may be opportunity and other costs for take up that are high for the poor; examples of trade-off between child labor and schooling and of cost recovery in health) Targeting indicator (dichotomic variable indicating beneficiary once there is take-up choice of public vs. private school) Benefit targeting (better indicator): Share of benefits going to the poor. 2 additional factors: Subsidy rate (price discount versus full cost of service) Quantity consumed (quantity of service consumed by household)
Incidence Analytical Framework (Decomposition of indicator) Five determinants of Ω A = access to service/good in neighborhood U = take-up of service/good given access A * U = actual household usage rate T = share of households receiving subsidy R = rate of subsidization Q = quantity of service/good consumed C = average cost of production & distribution R*Q*C = subsidy value among beneficiaries
Incidence Analysis Framework (Decomposition of indicator) Average benefit among the poor Bp = Ap*Up*Tp*Rp*Qp*C Average benefit among overall population Bn = An*Un*Tn*Rn*Qn*C A U T R Q Ω= P P P P P A U T R Q N N N N N
Incidence Analysis Framework (Sequential structure of the decomposition)
Main results for food aid in Liberia Omega Gamma Overall food aid 0.992 0.572 School feeding (meals, take home) 1.008 0.582 Food for community projects 0.899 0.519 Food for pregnant/breastfeeding women/children 0.774 0.447 Food for displaced families/refugees 0.949 0.548 Food for returning households 0.945 0.545 Other food aid 0.779 0.449
Main results for food aid in Burundi (and comparison with other public spending) Table 3: Performance of WFP targeting and other public transfers in 2006 Extreme Monetary poverty monetary poverty Food insecurity Kcal 1,900 Food insecurity Kcal 1,400 Ω γ Ω γ Ω γ Ω γ Food transfers Private transfers 0.82 0.56 0.75 0.37 0.92 0.54 0.97 0.36 WFP transfers 0.89 0.60 0.84 0.41 0.87 0.51 0.93 0.34 Education All cycles 1.08 0.72 0.77 0.51 0.90 0.60 0.55 0.37 Primary 1.10 0.73 0.79 0.53 0.91 0.61 0.55 0.37 Secondary 0.79 0.53 0.52 0.35 0.71 0.48 0.45 0.30 Higher 0.49 0.32 0.13 0.09 0.66 0.44 0.66 0.44 Health All consultations 0.89 0.60 0.60 0.40 0.78 0.52 0.47 0.32 Public hospital 0.81 0.54 0.54 0.36 0.70 0.47 0.40 0.27 Public dispensary 0.91 0.61 0.62 0.41 0.79 0.53 0.49 0.33 Infrastructure Electricity subsidies 0.10 0.06 0.06 0.03 0.24 0.12 0.16 0.05 Water subsidies 0.15 0.09 0.12 0.05 0.28 0.14 0.19 0.05 Source: Authors estimations using the QUIBB survey 2006
Conclusion on Public Policies In Africa, most countries don t have well targeted safety nets; hence interventions to deal with food crisis include indirect tax cuts, labor intensive public works and food aid. There are two key issues: (1) Many instruments are not well targeted Imported food consumption share of the poor is often low Leakage effects of public works wages are typically high Food aid may be received as much by the non-poor as by the poor, and it typoically entails large losgistic costs (transport) (2) The response seems at times to be targeted in large part to urban areas due to popular pressure, and less poor areas (ethical dilema responding to the shock or fighting long term poverty) But at the same time, what is the valid comparison in terms of what constitutes a good targeting performance? Many traditional benefit incidence exercises get it wrong, as can be shown by an illustration for primary schooling public spending in Sierra Leone
Conclusion on Public Policies Often the poor don t get that much Table 1: Benefit incidence analysis for public spending for primary education, 2003-04 Quintile Standard benefit incidence analysis based on population share Adjusted for needs (number of children in age of primary school) Adjusted for needs and costs (pupil teacher ratio) Adjusted for needs and costs (pupil teacher ratio and teacher quality) I (poorest) 105% 94% 83% 77% II 99% 92% 83% 80% III 115% 109% 115% 111% IV 105% 103% 109% 114% V (richest) 76% 105% 121% 134% Source: Authors estimation based on SLIHS 2003/2004.