This paper studies the price-setting behaviour in food products, using the microdata underlying the Portuguese Consumer Price Index (CPI). We document that, in each month, about 1 in 4 food items changed prices compared to the previous month, including promotions and sales. If these temporary price changes are excluded, the frequency drops to 1 out of 5, on average. Positive price changes were more frequent but had lower magnitudes than negative ones. There were strong heterogeneities in price-setting not only across products but also across industries and outlets. We find that, from 2009 to 2019, food inflation was primarily driven by the relative frequency of positive versus negative price changes, rather than changes in their size, consistent with standard New Keynesian calibrations of price rigidity. Finally, we report that price changes were more frequent at the producer than at the consumer level, but with a lower magnitude. Taken together, these results highlight that the frequency margin is the key driver of inflation dynamics and provide micro evidence supporting widely used macroeconomic calibrations of price rigidity.

This article uses a “mystery client” approach and visits the websites of National Statistical Offices and international microdata libraries to assess whether foundational microdata sets for countries in the Middle East and North Africa region are collected, up to date, and made available to researchers. The focus is on population and economic censuses, price data and consumption, labor, health, and establishment surveys. The results show that about half of the expected core data sets are being collected and that only a fraction is made available publicly. As a consequence, many summary statistics, including national accounts and welfare estimates, are outdated and of limited relevance to decision-makers. Additional investments in microdata collection and publication of the data once collected are strongly advised.

The advancement of computer systems has led to the existence of large and detailed existing computerized data sets, many of which contain useful information for studying the elderly and the aging process. This paper identifies several types of existing large data bases and provides examples of each. Strengths and weaknesses of working with existing data are discussed and strategies for conducting secondary data analysis of large data sets are outlined. Existing data sets represent large investments in the collection of individual data and have great potential to provide answers to important questions in the field of aging research.