Estimating Food Price Inflation from Partial Surveys
The traditional consumer price index is often produced at an aggregate level, using data from few, highly urbanized, areas. As such, it poorly describes price trends in rural or poverty-stricken areas, where large populations may reside in fragile...
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World Bank, Washington, DC
2021
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| Online Access: | http://documents.worldbank.org/curated/undefined/185851639662039407/Estimating-Food-Price-Inflation-from-Partial-Surveys http://hdl.handle.net/10986/36778 |
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okr-10986-367782021-12-24T05:10:39Z Estimating Food Price Inflation from Partial Surveys Andree, Bo Pieter Johannes INFLATION FOOD PRICES FOOD SECURITY FINANCIAL STABILITY MACHINE LEARNING The traditional consumer price index is often produced at an aggregate level, using data from few, highly urbanized, areas. As such, it poorly describes price trends in rural or poverty-stricken areas, where large populations may reside in fragile situations. Traditional price data collection also follows a deliberate sampling and measurement process that is not well suited for monitoring during crisis situations, when price stability may deteriorate rapidly. To gain real-time insights beyond what can be formally measured by traditional methods, this paper develops a machine-learning approach for imputation of ongoing subnational price surveys. The aim is to monitor inflation at the market level, relying only on incomplete and intermittent survey data. The capabilities are highlighted using World Food Programme surveys in 25 fragile and conflict-affected countries where real-time monthly food price data are not publicly available from official sources. The results are made available as a data set that covers more than 1200 markets and 43 food types. The local statistics provide a new granular view on important inflation events, including the World Food Price Crisis of 2007–08 and the surge in global inflation following the 2020 pandemic. The paper finds that imputations often achieve accuracy similar to direct measurement of prices. The estimates may provide new opportunities to investigate local price dynamics in markets where prices are sensitive to localized shocks and traditional data are not available. 2021-12-23T14:56:02Z 2021-12-23T14:56:02Z 2021-12 Working Paper http://documents.worldbank.org/curated/undefined/185851639662039407/Estimating-Food-Price-Inflation-from-Partial-Surveys http://hdl.handle.net/10986/36778 English Policy Research Working Paper;No. 9886 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper |
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Digital Repository |
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Foreign Institution |
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Digital Repositories |
| building |
World Bank Open Knowledge Repository |
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World Bank |
| language |
English |
| topic |
INFLATION FOOD PRICES FOOD SECURITY FINANCIAL STABILITY MACHINE LEARNING |
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INFLATION FOOD PRICES FOOD SECURITY FINANCIAL STABILITY MACHINE LEARNING Andree, Bo Pieter Johannes Estimating Food Price Inflation from Partial Surveys |
| relation |
Policy Research Working Paper;No. 9886 |
| description |
The traditional consumer price index
is often produced at an aggregate level, using data from
few, highly urbanized, areas. As such, it poorly describes
price trends in rural or poverty-stricken areas, where large
populations may reside in fragile situations. Traditional
price data collection also follows a deliberate sampling and
measurement process that is not well suited for monitoring
during crisis situations, when price stability may
deteriorate rapidly. To gain real-time insights beyond what
can be formally measured by traditional methods, this paper
develops a machine-learning approach for imputation of
ongoing subnational price surveys. The aim is to monitor
inflation at the market level, relying only on incomplete
and intermittent survey data. The capabilities are
highlighted using World Food Programme surveys in 25 fragile
and conflict-affected countries where real-time monthly food
price data are not publicly available from official sources.
The results are made available as a data set that covers
more than 1200 markets and 43 food types. The local
statistics provide a new granular view on important
inflation events, including the World Food Price Crisis of
2007–08 and the surge in global inflation following the 2020
pandemic. The paper finds that imputations often achieve
accuracy similar to direct measurement of prices. The
estimates may provide new opportunities to investigate local
price dynamics in markets where prices are sensitive to
localized shocks and traditional data are not available. |
| format |
Working Paper |
| author |
Andree, Bo Pieter Johannes |
| author_facet |
Andree, Bo Pieter Johannes |
| author_sort |
Andree, Bo Pieter Johannes |
| title |
Estimating Food Price Inflation from Partial Surveys |
| title_short |
Estimating Food Price Inflation from Partial Surveys |
| title_full |
Estimating Food Price Inflation from Partial Surveys |
| title_fullStr |
Estimating Food Price Inflation from Partial Surveys |
| title_full_unstemmed |
Estimating Food Price Inflation from Partial Surveys |
| title_sort |
estimating food price inflation from partial surveys |
| publisher |
World Bank, Washington, DC |
| publishDate |
2021 |
| url |
http://documents.worldbank.org/curated/undefined/185851639662039407/Estimating-Food-Price-Inflation-from-Partial-Surveys http://hdl.handle.net/10986/36778 |
| _version_ |
1764485876960722944 |