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CPI bias and its implications for poverty reduction in Africa (Inglês)

International poverty estimates for countries in Africa commonly rely on national consumer price indexes to adjust trends in nominal consumption over time for changes in the cost of living. However, the consumer price index is subject to various types of measurement bias. This paper uses Engel curve estimations to assess bias in the consumer price index and its implications for estimated poverty trends. The results suggest that in 11 of 16 Sub-Saharan African countries in this study, poverty reduction may be understated because of consumer price index bias. With correction of consumer price index bias, poverty in these countries could fall between 0.8 and 5.7 percentage points per year faster than currently thought. For two countries, however, the paper finds the opposite trend. There is no statistically significant change in poverty patterns after adjusting for consumer price index bias for the other three countries.

Detalhes

  • Autor

    Dabalen,Andrew L., Gaddis,Isis, Nguyen,Nga Thi Viet

  • Data do documento

    2016/12/06

  • TIpo de documento

    Documento de trabalho sobre pesquisa de políticas

  • No. do relatório

    WPS7907

  • Nº do volume

    1

  • Total Volume(s)

    1

  • País

    África,

  • Região

    África,

  • Data de divulgação

    2016/12/06

  • Disclosure Status

    Disclosed

  • Nome do documento

    CPI bias and its implications for poverty reduction in Africa

  • Palavras-chave

    cost of living;budget share;Upper Middle Income Countries;cost of living index;Expenditure and Consumption Survey;consumer price index;international poverty line;change in poverty;difference in poverty;understanding of poverty;data collection method;share of children;measurement of poverty;purchasing power parity;incidence of poverty;food energy requirement;change cost;consumption over time;improved living standard;basket of good;household head age;parameters in equation;price for food;national household survey;household survey data;price of good;benefit to consumer;relative price;correction factor;inflation rate;sample period;consumption datum;food share;real income;poverty estimate;household characteristic;robustness check;price level;household size;poverty trend;price change;total consumption;base period;rural area;actual inflation;urban population;consumption pattern;food price;household consumption;urban household;urban price;survey period;summary statistic;estimation result;urban bias;household expenditure;national statistical;measured poverty;food expenditure;regression results;real consumption;base year;household income;measurement error;regression analysis;weighted average;population group;Poverty Analysis;delta method;inflation measure;Exchange Rates;urban cities;open access;poor household;standard error;downward bias;geographical location;consumption estimate;rural price;average household;income rise;quality improvement;transition period;transitional economy;household furnishings;alcoholic beverage;political unrest;personal care;temporal variation;data quality;consumption aggregate;household demographics;urban resident;demographic group;geographical coverage;informal sector;food basket;inflation experience;differential inflation;representative household;discount price;retail outlet;supply side;linear function;nutritional value;development policy;consumption growth;statistical system;measuring poverty;input data;literature review;hedonic estimation;reduction rate;price quotation;survey design;regression coefficient;young child;intertemporal variation;simple model;demographic effect;constant term;individual household;national practices;basket weight;distribution channel;nonalcoholic beverages;marital status;negative relationship;explanatory variable;permanent income;spatial context;regression model;panel data;regional population;financial crisis;

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