metadata: authors: 0: author: Adoho,Franck M.
1: author: Backiny-Yetna,Prospere R.
2: author: Coulombe,Harold
3: author: Joseph, George
4: author: Tsimpo Nkengne,Clarence
5: author: Wodon,Quentin T.
count: Central Africa,Western Africa
docna: 0: docna: Potential impact of higher food prices on
            poverty : summary estimates for a dozen west and central
            African countries
docty: Policy Research Working Paper
subtopic: Climate Change and Agriculture,Labor & Employment Law,Crops and Crop Management Systems,Nutrition,Poverty Impact Evaluation,Poverty Monitoring & Analysis,Food Security,Inequality,Poverty Assessment,Poverty Diagnostics,Poverty Lines,Small Area Estimation Poverty Mapping
teratopic: Health, Nutrition and Population,Industry,Poverty Reduction
abstracts: cdata!: Concerns have been raised about the
            impact of rising food prices worldwide on the poor. To
            assess the impact of rising food prices in any particular
            country it is necessary to look at both the impact on food
            producers who are poor or near-poor and could benefit from
            an increase in prices and food consumers who are poor or
            near-poor and would loose out when the price increases. In
            most West and Central African countries, the sign (positive
            or negative) of the impact is not ambiguous because a
            substantial share of food consumption is imported, so that
            the negative impact for consumers is larger than the
            positive impact for net sellers of locally produced foods.
            Yet even if the sign of the impact is clear, its magnitude
            is not. Using a set of recent and comprehensive household
            surveys, this paper summarizes findings from an assessment
            of the potential impact of higher food prices on the poor in
            a dozen countries. Rising food prices for rice, wheat,
            maize, and other cereals as well as for milk, sugar and
            vegetable oils could lead to a substantial increase in
            poverty in many of the countries. At the same time, the data
            suggest that the magnitude of the increase in poverty
            between different countries is likely to be different.
            Finally, the data suggest that a large share of the increase
            in poverty will consist of deeper levels of poverty among
            households who are already poor, even if there will also be
            a larger number of poor households in the various countries.
repnme: repnme: Potential impact of higher food prices on
            poverty : summary estimates for a dozen west and central
            African countries
display_title: Potential impact of higher food
            prices on poverty : summary estimates for a dozen west and
            central African countries
listing_relative_url: /research/2008/10/9901009/potential-impact-higher-food-prices-poverty-summary-estimates-dozen-west-central-african-countries
topicv3: Food Security m1326439 199,Crop Production m1326627 174,Poverty m1326687 695,Poverty Measurement and Analysis m1326941 707,Household Surveys m1504071 2761
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Health, Nutrition and Population,Industry,Poverty Reduction
no_of_pages: 33
author_keys: 0: author_key: 944874
1: author_key: 716668
2: author_key: 142072
3: author_key: 686209
4: author_key: 124543
model_extractions: WPS4745 Policy ReseaRch WoRking PaPeR 4745 Potential Impact of Higher Food Prices on Poverty: Summary Estimates for a Dozen West and Central African Countries _Quentin Wodon Clarence Tsimpo Prospere Backiny-Yetna George Joseph Franck Adoho Harold Coulombe_ The World Bank Human Development Network Development Dialogue on Values and Ethics October 2008
Policy ReseaRch WoRking PaPeR 4745

# **Abstract**

Concerns have been raised about the impact of rising food prices worldwide on the poor. To assess the impact of rising food prices in any particular country it is necessary to look at both the impact on food producers who are poor or near-poor and could benefit from an increase in prices and food consumers who are poor or near-poor and would loose out when the price increases. In most West and Central African countries, the sign (positive or negative) of the impact is not ambiguous because a substantial share of food consumption is imported, so that the negative impact for consumers is larger than the positive impact for net sellers of locally produced foods. Yet even if the sign of the impact is clear, its magnitude is not. Using a set of recent and comprehensive household surveys, this paper summarizes findings from an assessment of the potential impact of higher food prices on the poor in a dozen countries. Rising food prices for rice, wheat, maize, and other cereals as well as for milk, sugar and vegetable oils could lead to a substantial increase in poverty in many of the countries. At the same time, the data suggest that the magnitude of the increase in poverty between different countries is likely to be different. Finally, the data suggest that a large share of the increase in poverty will consist of deeper levels of poverty among households who are already poor, even if there will also be a larger number of poor households in the various countries.

This paper—a product of the Development Dialogue on Values and Ethics, Human Development Network—is part of a larger study by the Africa Chief Economist Office and the Development Dialogue on Values and Ethics on the impact of the food price crisis in Africa and the policy responses available to governments. This research was started in the Africa PREM department and benefits from funding from the Africa Region Regional Studies Program as well as the Belgium and Luxemburg Poverty Reduction Partnerships. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at qwodon@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Potential Impact of Higher Food Prices on Poverty: Summary Estimates for a Dozen West and Central African Countries**<sup>1</sup>

Quentin Wodon, Clarence Tsimpo, Prospere Backiny-Yetna, George Joseph, Franck Adoho, and Harold Coulombe JEL categories: I32, D1, Q12 Keywords: food price, poverty, Africa

> 1 This paper and the broader research project it is part of have benefitted from discussions with and/or comments from among others Douglas Addison, Harold Alderman, Antonella Bassani, Shanta Devarajan, Hinh Dinh, Wilfried Engelke, Louise Fox, Delfin Go, Ana Revenga, Sudhir Shetty, Kenneth Simler, Linda Van Gelder, Jan Walliser, Vera Wilhelm, and Hassan Zaman.  All potential mistakes or omissions remain obviously ours.

1
# **1. Introduction**

The issue of the increase in food prices has received renewed attention in recent months as the increase in prices worldwide has had large negative impacts on households (e.g., Ivanic and Martin, 2007; World Bank, 2008a and 2008b; IMF, 2008; Wodon and Zaman, 2008). In West and Central Africa, prices for rice, maize and other cereals have increased substantially since the end of 2007. This has led the authorities as well as development partners in many countries to consider a range of compensatory measures that could help offset part of the negative impact on the poor of this increase in prices. However, at least from a conceptual point of view, the net impact of an increase in food prices on the poor is not obvious. Indeed, when discussing the link between rice and other cereal prices and poverty, a key issue is to assess the double and opposite impact that a change in prices can have for producers who are poor or near the poverty line (who benefit from an increase in prices) and consumers who are poor or near the poverty line (who lose out when the price increases).

The techniques for the analysis of the short term producer and consumer impacts of food commodity price changes are well developed in the literature. Early work in this area was conducted by Deaton (1989) using data from Thailand (see also Singh et al., 1986). Similar methods have been used in sub-Saharan Africa among others by Barrett and Dorosh (1996) for Madagascar, Budd (1993) for Cote d’Ivoire, and Loening and Oseni (2007) for Ethiopia. These are also the methods that we use in this paper, which summarizes the evidence on the impact of higher food prices on poverty obtained from a dozen country case studies for West and Central Africa. In all of these country case studies, we find that food price increases tend to lead to an increase in poverty because the consumption effects dominate the production effects as many countries are net importers of food.

There has also been a literature on assessing whether in the medium to long term, the increase in prices is compensated by an increase in wages, among others for those workers who contribute to the production of food crops (see for example Ravallion, 1990; Boyce and Ravallion, 1991, Rashid, 2002; Christaensen and Demery 2007; and Ivanic and Martin, 2007). The findings from these studies suggest that wage offset compensate only in a limited way for the initial increase in food prices. Finally, there has also been a substantial amount of work looking at the impact of various policies to deal with food production and prices. This can be illustrated with the case of rice. Indonesia is a country that used to import substantial amounts of 2
rice, but where restrictions were progressively placed on imports in order to help local producers, with imports of rice actually banned after 2004. Using a general equilibrium model, Warr (2005) find that the ban on rice imports raised the price of domestically produced rice, and that this led to an increase in poverty by almost one percentage point (on the Indonesia story as well as for a more general discussion on the experience of governments in Asia to stabilize the price of rice, see Timmer and Dawe, 2007; see also Ravallion and van de Walle, 1991). Another paper on Indonesia by (Sumarto et al., 2005) using panel data suggests that the practice of subsidizing rice as part of a social safety net led to a reduction in the risk for household to be poor. Papers on Vietnam by Niimi et al. (2004) and Minot and Goletti (2000) suggest that the liberalization of rice exports probably led to a reduction in poverty despite an increase in the price of rice in the country, thanks essentially to increased rice production.

In this paper (and in the more detailed country case studies that this paper summarizes), we focus however strictly on assessing what could be the short-term impact on poverty of the increase in the price of cereals as well as selected other food items in West and Central Africa. The impact of a change in the price of most food items is not ambiguous because most of the foods consumed are imported or produced from imported goods as in the case of bread. For these goods, an increase in price will tend to result in higher poverty in the countries as a whole (even if some local producers will gain from this increase). At the same time, the data suggest that the magnitude of the increase in poverty between different countries is likely to be different. Finally, the data suggest that a large share of the increase in poverty will consist of deeper levels of poverty among households who are already poor, even if there will also be a larger number of poor households in the countries.

The paper is structured as follows. Section 2 presents briefly our data sources as well as our methodology. In section 3, we provide estimates of the overall impact of higher food prices on poverty. A brief conclusion follows.

# **2. Methodology and Data**

We consider here only the short term impact on poverty of higher food prices, as estimated by looking at the consumption and production of food by households. This means that we do not take into account potential medium to long term impacts arising for example from the fact that an increase in food prices may lead to higher wages for farm workers (as mentioned in 3
the introduction, findings from studies on medium term impacts suggest that wage gains compensate only in a very limited way only for the initial impact of food price shocks).

For the sake of simplicity, a number of assumptions have been used to provide the estimates or are implicit in the analysis. First, we assume that the cost of an increase in food prices for a household translates into an equivalent reduction of its consumption in real terms. This means that we do not take into account the price elasticity of demand which may lead to substitution effects and thereby help offset part of the negative effect of higher prices for certain food items. Similarly, an increase for producers in the value of their net sales of food translates into an increase of their consumption of equivalent size, and we again do not take into account the role that the price elasticity of supply may play here. As for food auto-consumed by producers (which represents a large share of total consumption), it is not taken into account in the simulations since changes in prices do not affect households when food is auto-consumed.

Poverty measures obtained after the increase in prices are then compared to baseline poverty measures to assess impacts. This implicitly means that we do not take into account the potential spill-over effects of the increase in food prices for the food items included in the analysis on the prices for items not included. Finally, for comparability purposes, all our simulations are based on the same price increases for all countries and all food items. In the more detailed country case studies, more information is provided in order to be able to look at the impact of different price increases, for example through interpolations.

We report the potential impacts of the higher food prices on three poverty measures: the headcount index, the poverty gap, and the squared poverty gap. As explained for example in Coudouel et al. (2002), the headcount index of poverty is simply the share of the population which is poor, i.e. the proportion of the population for whom consumption (per capita or per equivalent adult) _y_ is less than the poverty line _z_ . The poverty gap, which is often considered as representing the depth of poverty, is the mean distance separating the population from the poverty line, with the non-poor being given a distance of zero. The poverty gap is thus a measure of the poverty deficit of the entire population, where the notion of “poverty deficit” captures the resources that would be needed to lift all the poor out of poverty through perfectly targeted cash transfers. The squared poverty gap is often described as a measure of the severity of poverty. While the poverty gap takes into account the distance separating the poor from the poverty line, the squared poverty gap takes the square of that distance into account. When using 4
the squared poverty gap, the poverty gap is weighted by itself, so as to give more weight to the very poor. Said differently, the squared poverty gap takes into account the inequality among the poor. The headcount, the poverty gap, and the squared poverty gap are the first three measures of the FGT class of poverty measures (Foster et al., 1984). Denoting the poverty line by _z_ , the consumption of the household per person or per equivalent adult by _yi_ , the total population size by _n_ , and the number of the poor by _q_ , the general formula for the FGT class of poverty measures with a parameter α taking a value of zero for the headcount, one for the poverty gap, and two for the squared poverty gap is as follows: While the emphasis in policy discussions is often placed on changes in the headcount index, it is important to use the poverty gap or the squared poverty gap in addition to the headcount for evaluation or simulation purposes. Indeed, basing an evaluation or simulation on the headcount index only would consider as more effective policies which lift the richest of the poor (those close to the line) out of poverty. On the basis of the poverty gap and the squared poverty gap on the other hand, puts the emphasis on helping those who are further away from the line, the poorest of the poor. The distinction between poverty measures matters also to assess where the increase in poverty is highest. We may for example have situations under which the increase in the headcount index of poverty is higher in urban than rural areas, while the reverse is true for the poverty gap or squared poverty gap. In general, it is better to rely on the analysis of changes in the poverty gap or squared poverty gap than the headcount index, because the headcount index does not take into account how poor people are, while the other two poverty measures do.

A difficult question is whether increases in consumer prices do translate into increases in producer prices. At least two factors may dilute the impact of rising food prices on the incomes of farmers. First, production costs for farmers as well as transport costs are likely to be rising due to higher costs for oil-related products. Second, market intermediaries may be able in some cases to keep a large share of the increase in consumer prices for themselves without paying farmers much more for their crops. Because it is difficult to assess whether producers will benefit substantially from higher food prices, especially in the short term, we consider our estimates obtained when considering only the impact on consumers as an upper bound of the 5
impact of the rise in prices on poverty, and we interpret the results obtained when factoring in a proportional increase in incomes for net sellers or producers as a lower bound of the impact.

Table 1 provides the countries for which the estimations have been prepared. The data have been collected from the most recent available household survey for each country. The survey years range from 2003 in Guinea to 2007 in Liberia, so the data can reasonably be considered as accurately capturing the current consumption patterns of the population in the respective countries. The table includes the list of food items considered for the analysis in each country. The analysis is focused for the most part on rice, flour and bread, maize, vegetable oil, sugar, and milk, because these are food items that tend to be imported to a substantial extent, so that likely poverty impacts may be substantial (since there are no compensating impacts on the producer side). In some countries however, we consider also additional items, such as cassava and plantain in the Democratic Republic of Congo.

The fact that we consider different food items for the simulations in different countries implies that the poverty impacts estimated need not be strictly comparable, as typically we would tend to have higher estimated impacts in countries where we consider a larger number of food items so that these food items typically represent a larger share of total consumption. Thus, we certainly do not claim comparability between countries, but nevertheless the analysis does suggest some interesting results, including differences between countries in terms of the rough magnitude of the impacts that could be expected. For example if countries are highly dependent on rice imports for their food consumption they may well suffer more from increases in prices.

Finally, we consider here the impact of an increase in food prices of 25 percent and 50 percent. To have some consistency in the results, the same price increases are considered for the various countries, even though the actual price increases may be different in the various countries. The detailed country papers provide more simulations with varying degrees of price increases. The idea in those detailed country papers is to provide a sufficient number of brackets of price increases so that it is easy to approximate the impact for different actual price increases in any given country at different points in time, taking into account changes in prices that may occur over time. While the poverty impacts need not be linear in the level of the price increase, they are nevertheless for practical purposes monotonic in most cases, so that they can still be roughly interpolated from generic data provided for various levels of price increases.

6
# **3. Empirical Results**

# **_3.1. Headcount index of poverty_**

Table 2 presents results regarding the impact on poverty of the increases in prices for the goods listed in table 1 by country, together with data on the share of total consumption represented by these goods. These shares of total consumption range from 6.5 percent in Togo to 28.3 percent in the Democratic Republic of Congo and even 41.0 percent in Niger. Yet for two thirds of the countries, the food items included in the simulations account for less than 15 percent of total consumption. The summary data on the impact on the headcount index of poverty (i.e., the share of the population in poverty) of the higher food prices is given for two levels of price increase: 25 percent and 50 percent. As mentioned earlier, the lower bound impact on poverty is obtained by combining the consumer and producer impact, while the upper bound impact factors in gains for net sellers of food. In two countries (Burkina Faso and Senegal), due to lack of appropriate data on agricultural production in the surveys, we compute only the upper bound estimates.

Consider the increase in poverty stemming from a 50 percent increase in prices. At the national level the upper bound estimates suggest that the increase in the headcount index of poverty varies from 1.8 percentage point in Ghana to 9.6 points in Senegal. The differences in impacts are due in part to the fact that the sets of goods considered for the simulations in the various countries represent different shares of total consumption. In Ghana the goods account for 7.7 percent of total consumption versus 20.5 percent in Senegal. If we look at the impact on poverty per percentage point of consumption accounted for by the food items included in the analysis, the poverty impact varies from 0.17 point in the DRC to 0.47 point in Senegal. If we were looking at the poverty gap measure of poverty, we would probably have a smaller range of impacts per percentage point of consumption included in the food items used for the simulations.

The impacts vary between countries, and between urban and rural areas within countries. In many countries, the poverty impacts are larger in percentage points in urban than in rural areas, but this is not always the case. In Ghana, Senegal, and Liberia, the poverty impact is actually larger in rural areas than in urban areas. In Ghana, this is essentially because poverty is low in urban areas in comparison to other countries. As Ghana’s urban population is better off, only a small percentage of urban dwellers fall into poverty with the price shock. In Senegal and Liberia, this is in part because a large share of food consumption in the country is imported. This 7
in turn means that even the rural poor suffer a lot from the impact of the price shocks. When data are available for the capital city separately from other urban areas (in Senegal and Togo), we find that impacts are largest in urban areas outside of the capital city.

The average increase in the headcount of poverty with a 50 percent increase in prices is 4.4 percentage points when only the impact on the consumer side is taken into account. This falls to 2.5 percentage points when producer impacts are counted for. Figure 1 provides a comparison of the upper and lower bound estimates at the national level. The differences are smallest for Niger, Liberia, and Gabon. These are three countries with substantial net imports of food (Senegal is in a similar situation, but not shown on the Figure since we do not have lower bound estimates for that country). In addition, in Liberia and Niger, while local food production is important, much of this local production is auto-consumed, and thereby is taken into account neither in the upper nor in the lower bound poverty estimates. In urban areas (counting separately the capital city and other urban areas when the data are available to do so), the average upper bound impact across all countries is 5.2 percentage points, and this falls to 3.7 points with producer gains. This drop may appear to be large, but many urban households are net producers of food, especially outside of the capital cities. In rural areas, the average upper bound impact is 4.1 points, falling to 2.2 points when factoring in producer gains.

These impacts are large. For example, an average 3.5 percentage point impact at the national level for all of sub-Saharan Africa, which has a total population of more than 800 million, would imply that the food crisis could lead to an increase in poverty of close to 30 million persons. In addition, all households who are already in poverty would be even poorer as well, an issue to which we will turn in the next section.

In Figure 2, the upper bound impacts for the increase in the price of rice alone are provided. This is the only commodity which was included in all of the sets of food items considered for the twelve countries. It is an important commodity, especially in Liberia, Senegal, Guinea and Sierra Leone where it represents a very large share of the food basket of the population. Rice is also important because West and Central African countries are typically net importers (and in some countries such as Senegal, virtually all the rice consumed is imported), and the price of rice has increased very substantially in recent months. In addition, available data suggests that in those countries where both local and imported rice are consumed, the price of both types of rice move very closely together, so that an increase in the price of imported rice 8
does translate into an increase in the price of locally produced rice. As is clear from the data presented in Figure 2, a 50 percent increase in the price of rice alone could lead to an increase in the headcount of poverty of 2.2 percentage points in the countries in the sample, and much more in some cases. Importantly, the lower bound estimates for the impact of rice shocks are not much lower than the upper bound because much of the locally produced rice is auto-consumed in the countries that do produce rice.

# **_3.2. Poverty gap and squared poverty gap_**

Tables 3 and 4 provide the impacts of the increase in food prices on the poverty gap and the squared poverty gaps. While for the headcount, the impact was often larger in urban areas than in rural areas, this is not the case for the poverty gap. In many countries, at least in terms of percentage points, the impact is now larger in rural than in urban areas, especially when looking at the upper bound impacts and the squared poverty gap. For example, in Burkina Faso, the upper bound increase in the headcount with a 50 percent increase in prices was at 2.8 percentage points in urban areas, versus 1.8 percentage point in rural areas. When using the poverty gap instead, the increase in rural areas at 1.1 percentage point is now larger than the increase in urban areas at 0.9 percentage point. With the squared poverty gap, the increase in rural areas at 0.6 percentage point is almost three times as large as the increase in urban areas, at 0.3 percentage points. Thus, even though the food price increase may generate in percentage terms a larger increase in the share of the poor in urban than in rural areas, the increase in poverty when one takes into account how far the poor are from the poverty line is larger in rural areas.

Considering the proportional changes in the poverty gap measures which are easier to interpret in intuitive terms than the changes in the squared poverty gap, we see that the increase in poverty is potentially large indeed. The poverty gap increases at the national level by two percent in the Democratic Republic of Congo (although this is from a very high base since this is the poorest country in the sample), six percent in Togo, seven percent in Burkina Faso and Ghana, eight percent in Guinea, Nigeria and Sierra Leone, 13 percent in Mali, 14 percent in Niger, 16 percent in Liberia, 17 percent in Gabon, and finally 31 percent in Senegal.

Another important finding is provided in tables 5 and 6, which give the shares of the increase in the poverty gap and the squared poverty gap that are due to an increase in how poor those who were initially poor before the shock are becoming due to the shock, as opposed to the 9
increase in the poverty gap or squared poverty gap that comes from household who have become poor, but were not poor before the shock. The findings are revealing: an overwhelming majority of the increase in the poverty gap and the squared poverty gap are due to higher levels of poverty among households who were already poor before the shock.

# **4. Conclusion**

This paper has provided a summary of analytical work conducted at the country level on the potential impact of higher food prices on poverty in West and Central Africa. We find that with a 50 percent increase in prices for selected food items, the average increase in the share of the population in poverty would be between 2.5 and 4.4 percentage points. The average impact would be between 3.7 and 5.2 percentage points in urban areas, and between 2.2 and 4.1 points in rural areas. These impacts are large. If the impact is at about 3.5 percentage point for a typical country, in sub-Saharan Africa as a whole, the food price crisis could lead to close to 30 million additional persons falling into poverty. The impacts are even larger in proportionate terms if we consider the increase in the poverty gap or the squared poverty gap as opposed to the increase in the headcount index of poverty.

The empirical analysis also suggests that while the increase in the headcount index is often larger in urban than in rural areas, the reverse is true for the increase in the poverty gap and the squared poverty gap. Moreover, it can be shown that most of the increase in the poverty gap or squared poverty gap is due to an increase in how much poorer those who were already poor are becoming, as opposed to the contribution to poverty of the “new poor” due to the shock. This suggests that policy responses to the crisis may have to focus more on helping the poor who are being made even more vulnerable by the price increase, as opposed to focusing on the “new poor” due to the shock.

10
# **References**

Boyce, J. K., and M. Ravallion, 1991, A Dynamic Econometric Model of Agricultural Wage Determination in Bangladesh, Oxford Bulletin of Economics and Statistics, 53(4): 361-76 Budd, J. W., 1993, Changing Food Prices and Rural Welfare: A Non-Parametric Examination of the Cote d’Ivoire, Economic Development and Cultural Change, 41(3): 587-603.

Coudouel, A., J. Hentschel, and Q. Wodon, 2002, Poverty Measurement and Analysis, in J. Klugman, editor, A Sourcebook for Poverty Reduction Strategies, Volume 1: Core Techniques and Cross-Cutting Issues, World Bank, Washington, DC.

Barrett, C. D. and P. A. Dorosh, 1996, Farmers' Welfare and Changing Food Prices: Nonparametric Evidence from Rice in Madagascar, <u>American Journal of Agricultural Economics, 78(3): 656-69.</u> Christiaensen, L. and L. Demery, 2007, <u>Down to Earth: Agriculture and Poverty Reduction in Africa, Directions in Development, World Bank, Washington, D.C.</u> Coudouel, A., J. Hentschel, and Q. Wodon, 2002, Poverty Measurement and Analysis, in J. Klugman, editor, A Sourcebook for Poverty Reduction Strategies, Volume 1: Core Techniques and Cross-Cutting Issues, World Bank, Washington.

Coulombe, H., and Q. Wodon, 2007, Poverty, Livelihoods and Access to Basic Services in Ghana, in World Bank, <u>Ghana: Meeting the Challenge of Accelerated and Shared Growth (Country Economic Memorandum), Report No. 40934-GH, Volume III: Background Papers,</u> Washington, DC.

Deaton, A., 1989, Rice Prices and Income Distribution in Thailand: A Non-Parametric Analysis, <u>The Economic Journal, 99(395):1-37.</u> Foster, J. E., J. Greer, E. Thorbecke, 1984, A Class of Decomposable Poverty Indices, <u>Econometrica</u> 52:761-766.

International Monetary Fund (2008) Food and Fuel Prices: Recent Developments, Macroeconomic Impact, and Policy Responses, mimeo, Washington, DC: IMF.

Ivanic, M., and W. Martin, 2007, Implications of Higher Global food Prices for Poverty in LowIncome Countries, Policy Research Working paper 4594, World Bank, Washington, DC.

Loening, J., and G. Oseni, 2007, Approximating Urban and Rural Welfare Effects of Food Price Inflation in Ethiopia, mimeo, World Bank, Washington, DC.

Minot, N, and F. Goletti, 1998, Export Liberalization and Household Welfare: The Case of Rice in Vietnam, American Journal of Agricultural Economics, 80(4): 738-49.

11
Niimi, Y., P. Vasudeva-Dutta, and A. L. Winters, 2004, Storm in a Rice Bowl: Rice Reform and Poverty in Vietnam in the 1990s, Journal of the Asia Pacific Economy, 9(2):170-190.

Ravallion, M. 1990, Welfare changes of food price changes under induced wage responses: Theory and evidence for Bangladesh, Oxford Economic Papers, 42: 574–85.

Ravallion, M. and D. van de Walle, 1991. The impact on poverty of food pricing reforms: a welfare analysis for Indonesia, Journal of Policy Modeling, 13(2):281-99.

Rashid, S. 2002, Dynamics of Agricultural Wage and Rice Price in Bangladesh: a Reexamination, MSSD Discussion Paper No. 44, International Food Policy Research Institute, Washington DC.

Singh, I., L. Squire, and J. Strauss, 1986, <u>Agricultural Household Models: Extensions and Applications, Johns Hopkins University Press, Baltimore.</u> Sumarto, S., A. Suryahadi, and W. Widyanti, 2005, Assessing the Impact of Indonesian Social Safety Net Programmes on Household Welfare and Poverty Dynamics, <u>European Journal of Development Research, 17(1): 155-77.</u> Timmer, C. P., and D. Dawe, 2007, Managing Food Price Instability in Asia: A Macro Food Security Perspective, Asian Economic Journal, 21(1): 1-18.

Warr, P., 2005, Food Policy and Poverty in Indonesia: A General Equilibrium Analysis, <u>Australian Journal of Agricultural and Resource Economics, 49(4): 429-51.</u> Wodon, Q., and H. Zaman, 2008, Poverty Impact of Higher Food Prices in Sub-Saharan Africa and Policy Responses, mimeo, World Bank, Washington, DC.

Wodon, Q., C. Tsimpo, P. Backiny-Yetna, G. Joseph, F. Adoho, and H. Coulombe, 2008a, Impact of Higher Food Prices on Poverty in West and Central Africa, mimeo, World Bank, Washington, DC.

World Bank, 2008a, Addressing the Food Crisis: The Need for Rapid and Coordinated Action, Background paper for the Finance Ministers Meetings of the Group of Eight, Poverty Reduction and Economic Management Network, Washington, DC World Bank, 2008b, <u>Guidance for Responses from the Human Development Sectors to Rising Food and Fuel prices, Human Development Network, Washington, DC.</u> 12
**<u>Table 1: Food items considered for simulating the potential impact of higher food prices on poverty</u>**

|Country|Household Survey|FoodItemsTaken into accountforsimulations|
|---|---|---|
|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|

Source: Authors’ estimation using respective household surveys.

13
**<u>Table 2: Potential Impact on Headcount Index of Poverty of Higher Food Prices in Africa</u>**

|**Country**|**Share in**<br>**Consumption**|**Baseline**<br>**Headcount**|**Upper bound**<br>**Impact**<br>**(Consumption)**<br>**25% increase**|**Upper bound**<br>**Impact**<br>**(Consumption)**<br>**50% increase**|**Lower Bound**<br>**Impact**<br>**(Cons. & Prod.)**<br>**25% increase**|**Lower Bound**<br>**Impact**<br>**(Cons. & Prod.)**<br>**50% increase**|
|---|---|---|---|---|---|---|
|Burkina Faso Nat.|6.8|**46.4**|47.5|48.4|**-**|**-**|
|Burkina Faso Urban|6.0|**19.9**|21.4|22.7|**-**|**-**|
|Burkina Faso Rural|8.3|**52.3**|53.3|54.1|-|**-**|
|Ghana National|7.7|**28.5**|29.6|30.4|29.2|29.7|
|Ghana Urban|6.6|**10.7**|11.5|11.8|11.4|11.7|
|Ghana Rural|9.0|**39.3**|40.4|41.6|40.0|40.5|
|Liberia National|22.8|**63.8**|67.1|69.8|66.6|69.4|
|Liberia Urban|14.6|**55.1**|57.8|60.5|57.6|60.4|
|Liberia Rural|29.2|**67.7 **|71.2|74.0|70.6|73.4|
|Senegal National|20.5|**50.8**|55.9|60.4|-|-|
|Senegal Dakar|15.8|**32.5**|37.4|41.2|-|-|
|Senegal Other Urban|22.3|**38.8**|43.9|50.2|-|-|
|Senegal Rural|24.9|**61.9**|67.1|71.4|-|-|
|Sierra Leone N|11.7|**66.4**|67.8|69.6|67.2|68.5|
|Sierra Leone U|6.4|**47.0**|48.6|51.4|48.5|50.9|
|Sierra Leone R|18.2|**78.6**|79.9|81.0|79.0|79.6|
|Togo National|6.5|**61.6**|62.7|63.7|62.6|63.6|
|Togo Lomé|5.6|**24.4**|24.9|25.8|24.9|25.8|
|Togo Other Urban|6.9|**54.5**|55.6|57.4|55.6|57.3|
|Togo Rural|7.1|**74.3**|75.4|76.4|75.4|76.3|
|RDC National|28.3|**71.3**|73.9|76.2|72.6|73.7|
|RDC Urban|23.5|**61.5**|65.1|68.4|64.9|68.1|
|RDC Rural|32.7|**75.7 **|77.8|79.7|76.0|76.2|
|Guinea National|13.0|**49.1**|50.7|52.1|50.0|50.7|
|Guinea Urban|9.4|**23.5**|26.6|29.0|26.6|29.0|
|Guinea Rural|16.1|**59.9**|60.8|61.7|59.8|59.8|
|Gabon National|10.7|**32.7**|34.5|36.7|34.3|36.2|
|Gabon Urban|11.3|**29.8**|31.7|34.0|31.6|33.8|
|Gabon Rural|8.4|**44.6**|45.9|47.8|45.2|46.2|
|Mali National|13.4|**47.5**|50.1|52.8|49.2|50.9|
|Mali Urban|15.9|**25.5**|28.8|31.3|28.4|30.7|
|Mali Rural|11.9|**57.6**|60.0|62.7|58.8|60.3|
|Niger National|41.0|**62.1**|66.1|70.0|65.9|69.6|
|Niger Urban|26.1|**44.1**|47.4|51.8|47.4|51.8|
|<br>Niger Rural|47.1|**65.7**|69.9|73.6|69.7|73.2|
|Nigeria National|9.80|**54.68**|56.20|57.77|55.19|55.65|
|Nigeria Urban|11.48|**43.13**|45.06|47.14|43.81|44.48|
|<br>Nigeria Rural|8.22|**63.80**|65.00|66.16|64.18|64.46|

Source: Authors’ estimation using respective household surveys.

14
**<u>Table 3: Potential Impact on Poverty Gap of Higher Food Prices in Africa</u>**

|**Country**|**Share in**<br>**Consumption**|**Baseline**<br>**Poverty Gap**|**Upper bound**<br>**Impact**<br>**(Consumption)**<br>**25% increase**|**Upper bound**<br>**Impact**<br>**(Consumption)**<br>**50% increase**|**Lower Bound**<br>**Impact**<br>**(Cons. & Prod.)**<br>**25% increase**|**Lower Bound**<br>**Impact**<br>**(Cons. & Prod.)**<br>**50% increase**|
|---|---|---|---|---|---|---|
|Burkina Faso Nat.|6.8|**15.6**|16.1|16.7|**-**|**-**|
|Burkina Faso Urban|6.0|**5.5**|5.9|6.4|**-**|**-**|
|Burkina Faso Rural|8.3|**17.9**|18.4|19.0|-|**-**|
|Ghana National|7.7|**9.6**|9.9|10.3|9.7|9.9|
|Ghana Urban|6.6|**3.1**|3.3|3.4|3.2|3.4|
|Ghana Rural|9.0|**13.5**|14.0|14.4|13.7|13.9|
|Liberia National|22.8|**24.4**|26.3|28.3|26.2|28.1|
|Liberia Urban|14.6|**20.2**|22.0|23.8|21.9|23.8|
|Liberia Rural|29.2|**26.3**|28.2|30.3|28.1|30.0|
|Senegal National|20.5|**16.4**|18.8|21.5|-|-|
|Senegal Dakar|15.8|**8.3**|9.7|11.4|-|-|
|Senegal Other Urban|22.3|**10.8**|12.9|15.4|-|-|
|Senegal Rural|24.9|**21.5**|24.4|27.6|-|-|
|Sierra Leone N|11.7|**27.5**|28.6|29.7|28.1|28.7|
|Sierra Leone U|6.4|**16.3**|17.1|17.9|16.9|17.6|
|Sierra Leone R|18.2|**34.6**|35.8|37.1|35.1|35.6|
|Togo National|6.5|**22.9**|23.5|24.2|23.5|24.1|
|Togo Lomé|5.6|**5.8**|6.1|6.4|6.1|6.4|
|Togo Other Urban|6.9|**16.8**|17.4|18.2|17.4|18.1|
|Togo Rural|7.1|**29.3**|30.1|30.8|30.0|30.7|
|RDC National|28.3|**32.2**|32.4|32.7|32.3|32.5|
|RDC Urban|23.5|**26.2**|26.5|26.9|26.5|26.9|
|RDC Rural|32.7|**34.9**|35.1|35.2|34.9|35.0|
|Guinea National|13.0|**17.2**|17.9|18.6|17.3|17.6|
|Guinea Urban|9.4|**6.1**|6.8|7.7|6.8|7.7|
|Guinea Rural|16.1|**21.9**|22.5|23.2|21.7|21.7|
|Gabon National|10.7|**10.0**|10.8|11.7|10.7|11.5|
|Gabon Urban|11.3|**8.5**|9.4|10.3|9.3|10.2|
|Gabon Rural|8.4|**16.0**|16.7|17.5|16.4|17.0|
|Mali National|13.4|**16.7**|17.6|18.8|17.1|17.8|
|Mali Urban|15.9|**7.8**|8.6|9.8|8.5|9.5|
|Mali Rural|11.9|**20.8**|21.8|22.9|21.1|21.6|
|Niger National|41.0|**25.9**|26.6|29.6|26.5|29.4|
|<br>Niger Urban|26.1|**15.3**|17.6|20.2|17.6|20.1|
|Niger Rural|47.1|**25.9**|28.5|31.5|28.3|31.2|
|Nigeria National|9.80|**22.5**|23.3|24.2|16.6|17.0|
|Nigeria Urban|11.48|**17.0**|17.8|18.7|17.3|17.6|
|Nigeria Rural|8.22|**26.8**|27.6|28.4|27.0|27.3|

Source: Authors’ estimation using respective household surveys.

15
**<u>Table 4: Potential Impact on Squared Poverty Gap of Higher Food Prices in Africa</u>**

|**Country**|**Share in**<br>**Consumption**|**Baseline**<br>**Squared**<br>**Poverty Gap**|**Upper bound**<br>**Impact**<br>**(Consumption)**<br>**25% increase**|**Upper bound**<br>**Impact**<br>**(Consumption)**<br>**50% increase**|**Lower Bound**<br>**Impact**<br>**(Cons. & Prod.)**<br>**25% increase**|**Lower Bound**<br>**Impact**<br>**(Cons. & Prod.)**<br>**50% increase**|
|---|---|---|---|---|---|---|
|Burkina Faso Nat.|6.8|**7.1**|7.4|7.7|**-**|**-**|
|Burkina Faso Urban|6.0|**2.2**|2.3|2.5|**-**|**-**|
|Burkina Faso Rural|8.3|**8.2**|8.5|8.8|-|**-**|
|Ghana National|7.7|**4.6**|4.8|5.0|4.7|4.8|
|Ghana Urban|6.6|**1.3**|1.4|1.5|1.4|1.4|
|Ghana Rural|9.0|**6.6**|6.8|7.1|6.7|6.8|
|Liberia National|22.8|**12.7**|13.8|15.1|13.7|15.0|
|Liberia Urban|14.6|**10.4**|11.5|12.7|11.5|12.7|
|Liberia Rural|29.2|**13.7**|14.8|16.2|14.8|16.0|
|Senegal National|20.5|**7.5**|8.8|10.4|-|-|
|Senegal Dakar|15.8|**3.0**|3.7|4.4|-|-|
|Senegal Other Urban|22.3|**4.5**|5.4|6.7|-|-|
|Senegal Rural|24.9|**10.2**|11.9|13.9|-|-|
|Sierra Leone N|11.7|**14.4**|15.2|16.0|14.8|15.2|
|Sierra Leone U|6.4|**7.6**|8.0|8.6|7.9|8.3|
|Sierra Leone R|18.2|**18.7**|19.7|20.6|19.1|19.6|
|Togo National|6.5|**11.0**|11.4|11.8|11.3|11.7|
|Togo Lomé|5.6|**2.1**|2.2|2.3|2.2|2.3|
|Togo Other Urban|6.9|**7.0**|7.4|7.8|7.4|7.7|
|Togo Rural|7.1|**14.5**|14.9|15.4|14.9|15.3|
|RDC National|28.3|**18.0**|18.2|18.3|18.1|18.2|
|RDC Urban|23.5|**14.1**|14.3|14.6|14.3|14.6|
|RDC Rural|32.7|**19.8**|19.9|20.0|19.8|19.8|
|Guinea National|13.0|**8.1**|8.5|8.9|8.2|8.3|
|Guinea Urban|9.4|**2.4**|2.7|3.1|2.7|3.1|
|Guinea Rural|16.1|**10.5**|10.9|11.3|10.4|10.5|
|Gabon National|10.7|**4.3**|4.7|5.2|4.7|5.1|
|Gabon Urban|11.3|**3.5**|3.9|4.4|3.9|4.3|
|Gabon Rural|8.4|**7.5**|8.0|8.4|7.8|8.1|
|Mali National|13.4|**8.0**|8.5|9.1|8.2|8.5|
|Mali Urban|15.9|**3.3**|3.7|4.2|3.7|4.1|
|Mali Rural|11.9|**10.2**|10.7|11.3|10.3|10.5|
|Niger National|41.0|**13.3**|13.8|15.8|13.8|15.7|
|<br>Niger Urban|26.1|**7.3**|8.6|10.3|8.6|10.3|
|Niger Rural|47.1|**13.3**|14.9|17.0|14.8|16.8|
|Nigeria National|9.80|**12.2**|12.7|13.2|7.9|8.1|
|Nigeria Urban|11.48|**9.2**|9.6|10.1|9.3|9.5|
|<br>Nigeria Rural|8.22|**14.6**|15.1|15.6|14.7|14.9|

Source: Authors’ estimation using respective household surveys.

16
**<u>Table 5: Share of increase in poverty gap due to deeper poverty among those initially poor</u>**

|**Country**|**Upper bound**<br>**Impact**<br>**(Consumption)**<br>**25% increase**|**Upper bound**<br>**Impact**<br>**(Consumption)**<br>**50% increase**|**Lower Bound**<br>**Impact**<br>**(Cons. & Prod.)**<br>**25% increase**|**Lower Bound**<br>**Impact**<br>**(Cons. & Prod.)**<br>**50% increase**|
|---|---|---|---|---|
|Burkina Faso Nat.|96.7|93.4|**-**|**-**|
|Burkina Faso Urban|89.6|83.2|**-**|**-**|
|Burkina Faso Rural|97.8|95.2|-|**-**|
|Ghana National|96.2|92.3|92.5|86.3|
|Ghana Urban|93.4|88.0|92.6|86.7|
|Ghana Rural|96.8|93.4|92.5|86.1|
|Liberia National|94.9|90.2|94.8|90.2|
|Liberia Urban|96.4|93.1|96.4|93.0|
|Liberia Rural|94.2|89.0|94.1|89.0|
|Senegal National|93.3|86.9|-|-|
|Senegal Dakar|91.4|83.6|-|-|
|Senegal Other Urban|91.1|83.5|-|-|
|Senegal Rural|94.2|88.4|-|-|
|Sierra Leone N|97.1|94.9|95.9|92.8|
|Sierra Leone U|95.6|92.1|94.8|91.1|
|Sierra Leone R|97.8|96.1|96.8|94.2|
|Togo National|98.4|96.8|98.3|96.6|
|Togo Lomé|98.6|95.7|98.6|95.7|
|Togo Other Urban|98.6|96.7|98.5|96.6|
|Togo Rural|98.4|96.9|98.3|96.7|
|RDC National|95.1|90.4|111.0|136.0|
|RDC Urban|94.9|89.9|95.5|91.1|
|RDC Rural|95.3|90.8|102.8|106.5|
|Guinea National|94.4|89.3|70.7|60.9|
|Guinea Urban|90.9|84.0|90.8|83.8|
|Guinea Rural|96.1|92.1|115.6|160.1|
|Gabon National|95.4|90.7|95.1|90.1|
|Gabon Urban|94.9|90.0|94.7|89.5|
|Gabon Rural|97.6|93.8|97.7|94.0|
|Mali National|89.9|82.3|81.1|71.3|
|Mali Urban|87.4|77.9|86.2|76.7|
|Mali Rural|91.0|84.2|75.3|66.1|
|Niger National|70.6|75.9|65.3|74.6|
|Niger Urban|93.6|87.1|93.5|87.0|
|Niger Rural|91.2|83.2|90.8|82.5|
|Nigeria National|96.0|91.8|94.1|88.7|
|Nigeria Urban|94.6|88.8|94.3|88.7|
|Nigeria Rural|97.3|94.4|93.8|88.7|

Source: Authors’ estimation using respective household surveys.

17
**<u>Table 6: Share of increase in squared poverty gap due to deeper poverty among those initially poor</u>**

|**Country**|**Upper bound**<br>**Impact**<br>**(Consumption)**<br>**25% increase**|**Upper bound**<br>**Impact**<br>**(Consumption)**<br>**50% increase**|**Lower Bound**<br>**Impact**<br>**(Cons. & Prod.)**<br>**25% increase**|**Lower Bound**<br>**Impact**<br>**(Cons. & Prod.)**<br>**50% increase**|
|---|---|---|---|---|
|Burkina Faso Nat.|99.8|99.2|**-**|**-**|
|Burkina Faso Urban|98.8|96.4|**-**|**-**|
|Burkina Faso Rural|99.9|99.6|-|**-**|
|Ghana National|99.8|99.3|99.7|98.7|
|Ghana Urban|99.6|98.4|99.6|98.3|
|Ghana Rural|99.9|99.4|99.7|98.8|
|Liberia National|99.6|98.5|99.6|98.5|
|Liberia Urban|99.8|99.2|99.8|99.2|
|Liberia Rural|99.5|98.1|99.5|98.2|
|Senegal National|99.4|97.7|-|-|
|Senegal Dakar|99.3|97.2|-|-|
|Senegal Other Urban|99.1|96.8|-|-|
|Senegal Rural|99.5|98.0|-|-|
|Sierra Leone N|99.9|99.6|99.8|99.5|
|Sierra Leone U|99.7|99.2|99.7|99.1|
|Sierra Leone R|99.9|99.7|99.9|99.6|
|Togo National|100.0|99.9|100.0|99.8|
|Togo Lomé|100.0|99.8|100.0|99.8|
|Togo Other Urban|100.0|99.9|100.0|99.9|
|Togo Rural|100.0|99.9|100.0|99.8|
|RDC National|99.6|98.4|100.8|107.8|
|RDC Urban|99.6|98.4|99.7|98.8|
|RDC Rural|99.6|98.4|100.2|101.2|
|Guinea National|99.6|98.4|96.1|93.0|
|Guinea Urban|99.3|97.5|99.3|97.5|
|Guinea Rural|99.7|98.7|101.2|116.3|
|Gabon National|99.7|98.7|99.7|98.6|
|Gabon Urban|99.6|98.5|99.6|98.5|
|Gabon Rural|99.9|99.5|99.9|99.4|
|Mali National|98.9|96.0|97.2|92.3|
|Mali Urban|98.6|94.8|98.4|94.6|
|Mali Rural|99.0|96.5|94.9|89.9|
|Niger National|96.9|94.0|96.5|93.7|
|Niger Urban|99.3|97.4|99.4|97.4|
|Niger Rural|98.8|95.4|98.8|95.2|
|Nigeria National|99.7|99.0|99.6|98.4|
|Nigeria Urban|99.6|98.3|99.5|98.3|
|Nigeria Rural|99.9|99.4|99.6|98.7|

Source: Authors’ estimation using respective household surveys.

18
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Annex 1: Burkina Faso - Detailed results for impact <u>of food price increase on headcount index in sample of West and Central African Countries</u>

|||||**25% increase**|**50% increase**|**25% increase**|**50% increase**|
|---|---|---|---|---|---|---|---|
|||||**Upper**|**Upper**|**Lower**|**Lower**|
||**Share in**|||**bound**|**bound**|**bound**|**bound**|
|**Food item**|<br>**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|<br>**Impact**<br>**(Cons. only)**|<br>**Impact**<br>**(Cons. only)**|<br>**Impact**<br>**(Cons.& Prod.) **|<br>**Impact**<br>**(Cons.& Prod.) **|
|||||**Burkina National**||||
|**Rice**|3.6|60.2|**46.4**|47.0|47.6|||
|**Bread**|0.7|35.6||46.4|46.4|||
|**Oil, butter**|1.1|74.9||46.6|46.8|||
|**Sugar**|0.9|67.4||46.6|46.7|||
|**Milk**|0.6|18.1||46.4|46.4|||
|All|6.8|91.9||47.5|48.4|||
|||||**Burkina Urban**||||
|**Rice**|2.9|53.7|**19.9**|20.9|21.9|||
|**Bread**|0.4|29.6||20.0|20.1|||
|**Oil, butter**|1.1|72.3||20.1|20.3|||
|**Sugar**|1.0|65.5||20.0|20.2|||
|**Milk**|0.6|15.4||19.9|19.9|||
|All|6.0|91.1||21.4|22.7|||
|||||**Burkina Rural**||||
|**Rice**|4.8|84.8|**52.3**|52.8|53.3|||
|**Bread**|1.1|58.5||52.3|52.3|||
|**Oil, butter**|1.2|85.0||52.5|52.6|||
|**Sugar**|0.7|74.5||52.5|52.6|||
|**Milk**|0.6|28.3||52.3|52.3|||
|All|8.3|94.6||53.3|54.1|||

Source: Authors’ estimation using country household survey data 20
Annex 2: DRC - Detailed results for impact <u>of food price increase on headcount index in sample of West and Central African Countries</u>

|||||**25% increase**|**50% increase**|**25% increase**|**50% increase**|
|---|---|---|---|---|---|---|---|
|**Food item**|**Share in**<br>**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|
||||**Democratic R**|**epublic of Congo**|**National**|||
|Rice|3.2|57.3|**71.3**|71.8|72.2|71.7|71.9|
|Cassava|10.2|91.9||71.8|72.3|71.6|71.8|
|Maize|5.9|50.7||72.0|72.6|71.5|71.6|
|Palm oil|4.0|96.2||71.9|72.4|71.6|71.8|
|Plantain|1.1|30.0||71.4|71.4|71.3|71.3|
|Wheat|1.8|35.1||71.6|71.7|71.6|71.7|
|Sugar|1.4|57.4||71.6|71.7|71.6|71.7|
|Milk|0.7|23.0||71.4|71.5|71.4|71.5|
|All|28.3|100.0||73.9|76.2|72.6|73.7|
||||**Democratic**|**Republic of Cong**|**o Urban**|||
|Rice|3.3|77.2|**61.5**|62.2|62.9|62.2|62.9|
|Cassava|5.5|89.4||62.3|63.4|62.2|63.2|
|Maize|6.3|78.7||62.2|63.0|62.1|62.9|
|Palm oil|2.6|96.1||62.0|62.4|62.0|62.4|
|Plantain|0.3|14.4||61.5|61.5|61.5|61.5|
|Wheat|3.0|70.2||62.0|62.3|62.0|62.3|
|Sugar|1.4|79.5||61.9|62.0|61.9|62.0|
|Milk|1.0|50.0||61.6|61.7|61.6|61.7|
|All|23.5|100.0||65.1|68.4|64.9|68.1|
||||**Democratic**|**Republic of Cong**|**o Rural**|||
|Rice|3.0|49.7|**75.7**|76.0|76.2|75.8|75.9|
|Cassava|14.5|92.8||76.0|76.3|75.7|75.6|
|Maize|5.6|39.9||76.3|76.8|75.7|75.4|
|Palm oil|5.3|96.2||76.3|76.8|75.9|76.0|
|Plantain|1.9|36.1||75.7|75.8|75.6|75.6|
|Wheat|0.7|21.5||75.8|75.9|75.8|75.9|
|Sugar|1.4|48.9||75.9|76.0|75.9|76.0|
|Milk|0.4|12.5||75.8|75.8|75.8|75.8|
|All|32.7|100.0||77.8|79.7|76.0|76.2|

Source: Authors’ estimation using country household survey data 21
Annex 3: <u>Gabon - Detailed results for impact of food price increase on headcount index in sample of West and Central African Countries</u>

|||||**25% increase**|**50% increase**|**25% increase**|**50% increase**|
|---|---|---|---|---|---|---|---|
||**Share in**|||**Upper**<br>**bound**|**Upper**<br>**bound**|**Lower**<br>**bound**|**Lower**<br>**bound**|
|**Food item**|<br>**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|<br> <br>**Impact**<br>**(Cons. only)**|<br>**Impact**<br>**(Cons. only)**|<br>**Impact**<br>**(Cons.& Prod.) **|<br>**Impact**<br>**(Cons.& Prod.) **|
|||||**Gabon National**||||
|Rice|3.0|91.4|**32.7**|33.2|33.8|33.2|33.8|
|Casava|1.9|86.4||33.0|33.2|32.8|33.0|
|Maize|0.3|40.0||32.7|32.7|32.7|32.7|
|Wheat|3.9|93.5||33.4|34.2|33.4|34.2|
|Palm oil and groundnut oil|1.7|90.6||33.0|33.2|33.0|33.2|
|All|10.7|97.3||34.5|36.7|34.3|36.2|
|||||**Gabon Urban**||||
|Rice|3.1|92.1|**29.8**|30.3|30.9|30.3|30.9|
|Casava|2.1|84.1||30.1|30.4|30.0|30.3|
|Maize|0.3|33.5||29.8|29.8|29.8|29.8|
|Wheat|4.2|94.8||30.5|31.4|30.5|31.4|
|Palm oil and groundnut oil|1.6|90.1||30.0|30.3|30.0|30.3|
|All|11.3|97.1||31.7|34.0|31.6|33.8|
|||||**Gabon Rural**||||
|Rice|2.5|88.5|**44.6**|44.9|45.1|44.9|45.1|
|Casava|1.0|95.7||44.6|44.7|44.2|44.0|
|Maize|0.2|65.8||44.6|44.6|44.6|44.5|
|Wheat|2.9|87.9||45.1|45.8|45.1|45.8|
|Palm oil and groundnut oil|2.0|92.6||44.8|44.9|44.8|44.8|
|All|8.4|98.2||45.9|47.8|45.2|46.2|

Source: Authors’ estimation using country household survey data 22
Annex 4: <u>Ghana - Detailed results for impact of food price increase on headcount index in sample of West and Central African Countries</u>

||**Share in**|||**25% increase**<br>**Upper**<br>**bound**|**50% increase**<br>**Upper**<br>**bound**|**25% increase**<br>**Lower**<br>**bound**|**50% increase**<br>**Lower**<br>**bound**|
|---|---|---|---|---|---|---|---|
|**Food item**|**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|**Impact**<br>**(Cons. only)**|**Impact**<br>**(Cons. only)**|**Impact**<br>**(Cons.& Prod.) **|**Impact**<br>**(Cons.& Prod.) **|
|||||<br>**Ghana National**||||
|Rice|3.1|74.6|**28.5**|29.0|29.5|29.0|29.4|
|Bread|1.9|84.6||28.9|29.2|||
|Flour|0.0|2.8||28.5|28.5|||
|Maize|2.7|66.9||28.8|29.0|28.3|28.2|
|All|7.7|96.3||29.6|30.4|29.2|29.7|
|||||**Ghana Urban**||||
|Rice|3.0|74.7|**10.7**|11.2|11.4|11.0|11.2|
|Bread|2.0|91.4||11.1|11.2|||
|Flour|0.0|2.6||10.7|10.7|||
|Maize|1.6|59.5||11.0|11.3|10.8|11.0|
|All|6.6|96.8||11.5|11.8|11.4|11.7|
|||||**Ghana Rural**||||
|Rice|3.2|74.4|**39.3**|39.8|40.5|39.8|40.4|
|Bread|1.7|79.4||39.7|40.1|||
|Flour|0.1|2.9||39.3|39.3|||
|Maize|4.1|72.6||39.5|39.8|38.9|38.7|
|All|9.0|95.9||40.4|41.6|40.0|40.5|

Source: Authors’ estimation using country household survey data 23
Annex 5: <u>Guinea - Detailed results for impact of food price increase on headcount index in sample of West and Central African Countries</u>

|**Food item**|**Share in**<br>**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|**25% increase**<br>**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**50% increase**<br>**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**25% increase**<br>**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|**50% increase**<br>**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|
|---|---|---|---|---|---|---|---|
||<br>||**G**<br>|<br>**uinea National**<br>|<br>|||
|Rice|13.0|90.7|**49.1**|50.7|52.1|50.0|50.7|
||||<br>|**Guinea Urban**<br>||||
|Rice|9.4|89.9|**23.5**|26.6|29.0|26.6|29.0|
|||||**Guinea Rural**<br>||||
|Rice|16.1|91.0|**59.9**|60.8|61.7|59.8|59.8|

Source: Authors’ estimation using country household survey data 24
Annex 6: Liberia - Detailed results for impact <u>of food price increase on headcount index in sample of West and Central African Countries</u>

|**Food item**|**Share in**<br>**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|**25% increase**<br>**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**50% increase**<br>**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**25% increase**<br>**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|**50% increase**<br>**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|
|---|---|---|---|---|---|---|---|
||<br>||**L**<br>|<br>**iberia National**<br>|<br>|<br>|<br>|
|Local Rice|9.6|60.1|**63.8**|64.3|64.8|63.9|64.3|
|Imported Rice|13.2|84.9||66.6|69.0|||
|All|22.8|99.0||67.1|69.8|66.6|69.4|
|||||**Liberia Urban**<br>||||
|Local Rice|1.1|17.1|**55.1**|55.2|55.3|55.1|55.2|
|Imported Rice|13.5|97.3||57.6|60.3|||
|All|14.6|98.6||57.8|60.5|57.6|60.4|
|||||**Liberia Rural**<br>||||
|Local Rice|16.2|80.0|**67.7**|68.4|69.0|67.8|68.4|
|Imported Rice|12.9|79.2||70.6|72.8|||
|All|29.2|99.2||71.2|74.0|70.6|73.4|

Source: Authors’ estimation using country household survey data 25
Annex 7: Mali - Detailed results for impact <u>of food price increase on headcount index in sample of West and Central African Countries</u>

||**Share in**|||**25% increase**<br>**Upper**<br>**bound**|**50% increase**<br>**Upper**<br>**bound**|**25% increase**<br>**Lower**<br>**bound**|**50% increase**<br>**Lower**<br>**bound**|
|---|---|---|---|---|---|---|---|
|**Food item**|**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|**Impact**<br>**(Cons. only)**|**Impact**<br>**(Cons. only)**|**Impact**<br>**(Cons.& Prod.) **|**Impact**<br>**(Cons.& Prod.) **|
|||||<br>**Mali National**||||
|Rice|7.2|95.1|**47.5**|48.9|50.2|48.3|49.1|
|Corn|4.2|91.0||48.2|49.4|47.9|48.8|
|Maize|0.6|48.1||47.5|47.6|47.4|47.4|
|Wheat|1.5|74.0||47.6|47.8|47.6|47.8|
|All|13.4|99.4||50.1|52.8|49.2|50.9|
|||||**Mali Urban**||||
|Rice|9.6|96.0|**25.5**|27.4|29.0|27.3|28.6|
|Corn|3.6|88.4||25.8|27.1|25.7|27.0|
|Maize|0.8|42.0||25.6|25.8|25.4|25.5|
|Wheat|1.9|83.6||25.7|25.9|25.7|25.9|
|All|15.9|99.4||28.8|31.3|28.4|30.7|
|||||**Mali Rural**||||
|Rice|5.7|94.6|**57.6**|58.9|60.1|58.1|58.6|
|Corn|4.5|92.7||58.6|59.7|58.2|58.9|
|Maize|0.4|51.7||57.7|57.7|57.6|57.5|
|Wheat|1.3|68.2||57.8|57.9|57.8|57.9|
|All|11.9|99.8||60.0|62.7|58.8|60.3|

Source: Authors’ estimation using country household survey data 26
Annex 8: <u>Niger - Detailed results for impact of food price increase on headcount index in sample of West and Central African Countries</u>

|**Food item**|**Share in**<br>**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|**25% increase**<br>**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**50% increase**<br>**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**25% increase**<br>**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|**50% increase**<br>**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|
|---|---|---|---|---|---|---|---|
|||||**Niger National**||||
|Rice Imported|4.4|54.7|**62.1**|62.8|63.6|62.7|63.5|
|Rice local|1.7|15.4||62.2|62.3|62.2|62.3|
|Total riz|6.1|67.7||63.0|63.8|62.9|63.8|
|Millet & Sorghum|30.6|94.2||64.5|67.2|64.4|67.1|
|Maize|4.3|30.4||62.5|63.0|62.5|63.0|
|All|41.0|98.5||66.1|70.0|65.9|69.6|
|||||**Niger Urban**||||
|Rice Imported|7.8|85.1|**44.1**|45.5|47.0|45.5|47.0|
|Rice local|1.0|11.2||44.4|44.5|44.3|44.4|
|Total riz|8.9|92.6||45.8|47.4|45.8|47.4|
|Millet & Sorghum|11.5|78.0||45.1|46.1|45.1|46.1|
|Maize|5.8|61.7||44.9|46.1|44.9|46.1|
|All|26.1|97.0||47.4|51.8|47.4|51.8|
|||||**Niger Rural**||||
|Rice Imported|3.1|48.5|**65.7**|66.3|66.9|66.2|66.8|
|Rice local|2.0|16.2||65.8|65.9|65.8|65.9|
|Total riz|5.0|62.6||66.5|67.1|66.4|67.1|
|Millet & Sorghum|38.4|97.5||68.4|71.5|68.3|71.3|
|Maize|3.7|24.0||66.1|66.4|66.1|66.4|
|All|47.1|98.9||69.9|73.6|69.7|73.2|

Source: Authors’ estimation using country household survey data 27
Annex 9: <u>Nigeria - Detailed results for impact of food price increase on headcount index in sample of West and Central African Countries</u>

|**Food item**|**Share in**<br>**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|**25% increase**<br> <br>**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**50% increase**<br>**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**25% increase**<br>**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|**50% increase**<br>**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|
|---|---|---|---|---|---|---|---|
||||<br>|**Nigeria National**<br>||||
|Rice|0.04|0.73|**54.68**|55.28|55.88|55.19|55.65|
|Corn and cornflour|0.01|0.35||54.81|54.88|54.65|54.57|
|Maize and Maize flour|0.01|0.42||54.86|55.03|54.73|54.80|
|Wheat flour and bread|0.01|0.70||54.90|55.12|54.90|55.12|
|cassava|0.03|0.65||55.10|55.42|55.07|55.35|
|All|0.10|0.95||56.20|57.77|55.19|55.65|
|||||**Nigeria Urban**||||
|Rice|0.05|0.72|**43.13**|43.83|44.56|43.81|44.48|
|Corn and cornflour|0.01|0.23||43.26|43.30|43.19|43.20|
|Maize and Maize flour|0.01|0.42||43.38|43.61|43.37|43.54|
|Wheat flour and bread|0.02|0.77||43.38|43.65|43.38|43.65|
|cassava|0.03|0.65||43.62|43.95|43.60|43.91|
|All|0.11|0.93||45.06|47.14|43.81|44.48|
|||||**Nigeria Rural**||||
|Rice|3.73|74.78|**63.80**|64.32|64.81|64.18|64.46|
|Corn and cornflour|0.75|44.90||63.92|64.02|63.69|63.54|
|Maize and Maize flour|0.59|41.93||63.92|64.05|63.71|63.69|
|Wheat flour and bread|1.17|64.73||64.00|64.18|64.00|64.18|
|cassava|1.99|65.11||64.16|64.46|64.12|64.37|
|All|8.22|97.35||65.00|66.16|64.18|64.46|

Source: Authors’ estimation using country household survey data 28
Annex 10: <u>Senegal - Detailed results for impact of food price increase on headcount index in sample of West and Central African Countries</u>

|||||**25% increase**|**50% increase**|**25% increase**|**50% increase**|
|---|---|---|---|---|---|---|---|
|||||**Upper**|**Upper**|**Lower**|**Lower**|
|**Food item**|**Share in**<br>**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|<br>**bound**<br>**Impact**<br>**(Cons. only)**|**bound**<br>**Impact**<br>**(Cons. only)**|**bound**<br>**Impact**<br>**(Cons.& Prod.) **|**bound**<br>**Impact**<br>**(Cons.& Prod.) **|
|||||**Senegal National**||||
|Rice|6.8|96.3|**50.8**|52.5|54.2|||
|Huiles végétales|4.5|95.8||51.6|52.7|||
|Sucre|3.0|99.2||51.4|52.1|||
|Bread|4.0|92.7||51.5|52.4|||
|Milk|2.1|79.6||51.1|51.4|||
|All|20.5|99.8||55.9|60.4|||
|||||**Senegal Dakar**||||
|Rice|4.5|95.4|**32.5**|33.8|35.5|||
|Huiles végétales|3.2|95.7||33.3|33.9|||
|<br>Sucre|1.8|99.6||33.1|33.7|||
|Bread|3.9|98.9||33.4|34.6|||
|Milk|2.5|99.4||33.1|33.5|||
|All|15.8|99.9||37.4|41.2|||
||||**Sene**|**gal Other Urban Ar**|**eas**|||
|Rice|6.9|94.2|**38.8**|40.7|42.2|||
|Huiles végétales|4.8|94.7||39.5|41.1|||
|<br>Sucre|3.0|99.3||39.5|40.7|||
|Bread|5.0|96.5||39.9|41.5|||
|Milk|2.6|96.0||39.2|39.5|||
|All|22.3|99.8||43.9|50.2|||
|||||**Senegal Rural**||||
|Rice|9.4|97.4|**61.9**|63.8|65.6|||
|Huiles végétales|5.8|96.2||62.9|64.0|||
|<br>Sucre|4.5|98.8||62.5|63.2|||
|Bread|3.8|88.2||62.5|63.0|||
|Milk|1.4|63.6||62.1|62.4|||
|All|24.9|99.7||67.1|71.4|||

Source: Authors’ estimation using country household survey data 29
Annex 11: <u>Sierra Leone - Detailed results for impact of food price increase on headcount index in sample of West and Central African Countries</u>

|**Food item**|**Share in**<br>**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|**25% increase**<br>**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**50% increase**<br>**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**25% increase**<br>**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|**50% increase**<br>**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|
|---|---|---|---|---|---|---|---|
||<br>||**Sierr**<br>|<br>**a Leone National**<br>|<br>|||
|Rice|11.7|96.4|**66.4**|67.8|69.6|67.2|68.5|
||||**Sier**<br>|**ra Leone Urban**<br>||||
|Rice|6.4|94.5|**47.0**|48.6|51.4|48.5|50.9|
||||**Sie**<br>|**rra Leone Rural**<br>||||
|Rice|18.2|97.7|**78.6**|79.9|81.0|79.0|79.6|

Source: Authors’ estimation using country household survey data 30
Annex 12: Togo - Detailed results for impact <u>of food price increase on headcount index in sample of West and Central African Countries</u>

|||||**25% increase**<br>|**50% increase**<br>|**25% increase**<br>|**50% increase**<br>|
|---|---|---|---|---|---|---|---|
|||||**Upper**|**Upper**|**Lower**|**Lower**|
|**Food item**|**Share in**<br>**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|**bound**<br>**Impact**<br>**(Cons. only)**|**bound**<br>**Impact**<br>**(Cons. only)**|**bound**<br>**Impact**<br>**(Cons.& Prod.) **|**bound**<br>**Impact**<br>**(Cons.& Prod.) **|
|||||**Togo National**||||
|**Rice**|3.5|92.2|**61.6**|62.2|62.9|62.2|62.8|
|**Bread**|0.6|27.0||61.7|61.8|||
|**Milk**|0.7|31.1||61.7|61.8|||
|**Huiles**|1.1|81.3||61.8|62.0|||
|**Sugar**|0.7|72.3||61.8|61.9|||
|All|6.5|97.4||62.7|63.7|62.6|63.6|
|||||**Togo Lomé**||||
|**Rice**|2.5|93.3|**24.4**|24.8|25.2|24.8|25.2|
|**Bread**|0.9|50.5||24.5|24.5|||
|**Milk**|0.9|56.0||24.5|24.5|||
|**Huiles**|0.7|91.3||24.5|24.6|||
|**Sugar**|0.5|86.3||24.4|24.5|||
|All|5.6|97.3||24.9|25.8|24.9|25.8|
||||**Togo**|**Other Urban Area**|**s**|||
|**Rice**|3.7|95.2|**54.5**|55.1|56.1|55.1|56.1|
|**Bread**|0.4|24.3||54.6|54.6|||
|**Milk**|0.7|34.5||54.6|54.7|||
|**Huiles**|1.3|89.3||54.8|55.0|||
|**Sugar**|0.7|73.3||54.8|54.8|||
|All|6.9|99.0||55.6|57.4|55.6|57.3|
|||||**Togo Rural**||||
|**Rice**|4.3|91.1|**74.3**|75.0|75.6|74.9|75.5|
|**Bread**|0.3|19.3||74.3|74.5|||
|**Milk**|0.4|21.3||74.4|74.5|||
|**Huiles**|1.4|75.8||74.5|74.7|||
|**Sugar**|0.8|67.1||74.4|74.6|||
|All|7.1|97.1||75.4|76.4|75.4|76.3|

Source: Authors’ estimation using country household survey data 31