whole – Alpura brand – 1lt carton”, “beef: ground –unbranded – unpackaged – by the bulk”, among many others.

INEGI follows a wide range of specifics within a certain generic category. The aim of casting a wide net is to gather as much information from markets as possible and to avoid capturing price behaviors that may be idiosyncratic to certain brands or varieties. In some cases, this wide net approach is also due to the fact that consumption habits and tastes vary regionally and the aim is to capture all habits across Mexico. A clear example of this is bean consumption: people from southern and central states tend to prefer (and consume more) black beans, whereas people from the north and the west usually consume pinto beans (and almost no black beans at all). Accordingly, INEGI’s price gatherings of the generic category “beans” in the south and the center of the country contain more information of the specific good “black beans” and much fewer price quotes of the specific “pinto beans”. The opposite is the case for cities in northern and western states.

In sum, INEGI’s database contains detailed information of the characteristics of wide range of goods. We were able to track specifics precisely and to distinguish them from other goods that may be similar. As a result, it was possible to analyze goods that are exactly the same across cities and time; we compared apples with apples. Also, INEGI’s wide coverage of generics means that price data for these may be heterogeneous within some generic categories. In principle, higher levels of heterogeneity should not pose a problem for our analysis. As long as a specific is widely covered across and within cities and is consistently followed through time, heterogeneity in the price data is, if anything, a potential advantage to exploit in the empirical strategy.

# Challenges when using the INPC data

While INEGI does follow all generics in all cities, there were cases where specifics surveyed in certain cities or regions did not match (strictly speaking) with the ones followed in other geographical areas. This seemed to be apparent independently of consumption habits and tastes or availability of certain specifics within a city. This sometimes happened because INEGI’s database is intended to be representative only at the national level, a requirement that allows degrees of freedom on goods selection across cities.

An illustrative example for this is soda. Soda is widely available basically everywhere and it comes in different presentations, sizes, flavors and brands. There is no doubt that many brands and sizes (for example, Coca Cola – 600 ml bottle) are sold in all cities and in practically all types of outlets. Thus, it would be expected to count with several price quotes in all cities of such common specifics. However, what we see is that INEGI collected price data on certain varieties and brands in some cities and other varieties and brands in other cities. The price of specific “Coca Cola – 600ml” is widely tracked in a set of cities, while in others the specific “Pepsi – 600ml” is tracked. We know that both specifics are widely available and sold in all cities, but the data collection does not reflect this.

This fact limited the number of exact matches of specific goods that could be compared across cities and represented a challenge to the empirical strategy. For some goods, we were able to obviate brand and analyze specifics of exactly same characteristics (variety, size, package, etc.) as if they were the same good. Unfortunately, we also had to drop entire generic categories simply because the set of generics was too heterogeneous and prices across these 9