Assessing the Geographic Impact of Higher Food Prices in Guinea
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World Bank, Washington, DC
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Telling a policy maker that poverty will
increase due to the recent increase in food prices is not
very useful; telling the policy makers where the impact is
likely to be larger is better, so that measures to cope with
the impact of the crisis can be targeted to areas that need
them the most. This paper shows how to use poverty mapping
techniques to assess where higher food prices are likely to
hurt the most using Guinea census and survey data as a case
study. The results suggest that in the case of a rice price
increase, the poorest areas of the country will not be the
hardest hit, especially if the potential positive impact of
higher food prices on rice producers is taken into account,
in which case poverty may decline in some of these areas
even if for the country as a whole poverty will increase
significantly due to the large share of rice in the
household consumption budget.
Palabras clave
AGRICULTURAL PRODUCTIVITY, ANTI-POVERTY, CALORIC INTAKE, CAPITA EXPENDITURE, CASE STUDY, CHANGE IN POVERTY, CONSUMER PRICES, CONSUMPTION AGGREGATE, CONSUMPTION DATA, CULTURAL CHANGE, DESCRIPTIVE STATISTICS, DEVELOPING COUNTRIES, DEVELOPMENT RESEARCH, DEVELOPMENT RESEARCH GROUP, DIET, DISAGGREGATED LEVEL, DISTRICT LEVEL, ECONOMETRIC MODELS, ECONOMIC ACTIVITIES, ECONOMIC DEVELOPMENT, ECONOMICS, ERROR TERM, ERROR TERMS, ESTIMATES OF POVERTY, ESTIMATION OF POVERTY, EXPLANATORY VARIABLES, FARM WORKERS, FARMERS, FOOD CONSUMPTION, FOOD IMPORTS, FOOD ITEMS, FOOD PRICE, FOOD PRICES, FOOD PRODUCTION, FOOD SUPPLY, GEOGRAPHIC PROFILE OF POVERTY, GINI INDEX, HEADCOUNT RATIO, HOUSEHOLD CONSUMPTION, HOUSEHOLD CONSUMPTION PER CAPITA, HOUSEHOLD INCOME, HOUSEHOLD INCOMES, HOUSEHOLD LEVEL, HOUSEHOLD SURVEY, HOUSEHOLD SURVEY DATA, HOUSEHOLD SURVEYS, HOUSING, HUMAN DEVELOPMENT, IMPACT OF SHOCKS, IMPACT ON POVERTY, INCOME, INCOME DISTRIBUTION, INCOME GAINS, INDEPENDENT VARIABLES, INEQUALITY, INEQUALITY MEASURES, LEVEL OF POVERTY, MEDIUM TERM, NATIONAL POVERTY, NATIONAL POVERTY HEADCOUNT, NEGATIVE EFFECT, NUMBER OF HOUSEHOLDS, PARAMETER ESTIMATES, PER CAPITA EXPENDITURE, POINT ESTIMATE, POLICY INTERVENTIONS, POLICY MAKERS, POLICY RESEARCH, POLICY RESEARCH WORKING PAPER, POOR, POOR AREAS, POOR HOUSEHOLDS, POPULATION SIZE, POPULATION SUBGROUPS, POVERTY ESTIMATES, POVERTY GAP, POVERTY GAP INDEX, POVERTY IMPACT, POVERTY INDICATORS, POVERTY MAP, POVERTY MAPPING, POVERTY MAPPING EXERCISE, POVERTY MAPPING INITIATIVE, POVERTY MAPPING METHODOLOGY, POVERTY MAPS, POVERTY MEASURE, POVERTY MEASURES, POVERTY PROFILE, POVERTY PROFILES, POVERTY PROGRAMS, POVERTY RATES, POVERTY REDUCTION, POVERTY SEVERITY, PRO-POOR, PROGRESS, PUBLIC POLICY, RURAL, RURAL AREAS, RURAL POOR, RURAL WELFARE, SAFETY NET, SAFETY NET PROGRAMS, SAFETY NETS, SIMULATIONS, SOCIAL ASSISTANCE, SOCIAL PROTECTION, SQUARED POVERTY GAP, SQUARED POVERTY GAP INDEX, STANDARD DEVIATION, STANDARD ERRORS, SUB-SAHARAN AFRICA, TARGETING, TRANSITION COUNTRIES, URBAN AREAS, URBAN POOR, URBAN POVERTY, VARIANCE-COVARIANCE MATRIX, VILLAGE LEVEL, WELFARE INDICATORS