Using Retail Scanner Data to Estimate CPI in Chile

June 1, 2023

In 2022, Cepei, together with LIRNEasia and other research centers from the Global South, created the SDG Acceleration Roadmap initiative that aimed to map public-private data collaborations and their contribution to achieving the Sustainable Development Goals (SDGs).

From Cepei, we hope that, through this research, public-private data partnerships will improve their potential to respond to the global context.

This Executive Summary provides insights into the partnership between several Chilean companies that collect retail barcode data and the Chile National Institute of Statistics (INE), which combines these data with traditional survey data to produce the Consumer Price Index (CPI).

Principales hallazgos

The use of scanner data by INE Chile to calculate monthly CPI is contributing to the generation of invaluable statistics both for officials responsible for setting economic and fiscal policy and reporting on SDG 11 indicators.

The public-private initiatives on which INE has embarked are now a model that, in the future, can be expanded and replicated in other areas where private-sector data are considered to have public value.

The COVID-19 pandemic severely limited INE's ability to obtain data through traditional face-to-face surveys. As a result, INE decided to innovate, learning from models used elsewhere and using scanner data to compile its CPI.

Main Findings

The use of scanner data by INE Chile to calculate monthly CPI is contributing to the generation of invaluable statistics both for officials responsible for setting economic and fiscal policy and reporting on SDG 11 indicators.

The public-private initiatives on which INE has embarked are now a model that, in the future, can be expanded and replicated in other areas where private-sector data are considered to have public value.

The COVID-19 pandemic severely limited INE's ability to obtain data through traditional face-to-face surveys. As a result, INE decided to innovate, learning from models used elsewhere and using scanner data to compile its CPI.

About the author

Hernán Muñoz

Economist of the University of Buenos Aires (UBA), with postgraduate studies in Finance (UBA), MSc in Public Policy (University of San Andrés), and currently, PhD candidate at Sapienza University of Rome. He has specialized in strategic planning, statistical capacity building and good practices. He was National Director of Planning, Institutional and International Relations of the National Institute of Statistics and Census (INDEC) of Argentina from 2015 to 2019. Since 2020, he collaborates with Cepei as Data Ecosystems Advisor.

Acerca del autor

Hernán Muñoz

Economist of the University of Buenos Aires (UBA), with postgraduate studies in Finance (UBA), MSc in Public Policy (University of San Andrés), and currently, PhD candidate at Sapienza University of Rome. He has specialized in strategic planning, statistical capacity building and good practices. He was National Director of Planning, Institutional and International Relations of the National Institute of Statistics and Census (INDEC) of Argentina from 2015 to 2019. Since 2020, he collaborates with Cepei as Data Ecosystems Advisor.

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