Conclusiones y proyecciones !

This project exploits a large micro-dataset gathered by the Bank of Mexico and used to calculate the Consumer Price Index (CPI) in Mexico. The original dataset comprises more than 18 million price records collected over a 205 semi-monthly

periods from the second half of June 2002 to December 2010.2

The geographical coverage and product-categories included in the micro-price sample are dictated by the 2000 National Survey of Households’ Income and Ex- penditures (Encuesta Nacional Ingreso y Gasto de los Hogares, ENIGH) conducted by the National Institute of Statistics and Geography (INEGI) of Mexico. Results from the household survey ensure that the CPI measures the price evolution of a representative basket of goods and services consumed by an average Mexican house- hold.

For this price survey, around 100 Banxico employees in 46 urban regions

across Mexico visited more than 20,000 retail outlets on a regular basis.3 _{A wide}

range of outlets were visited, from grocery stores, pharmacies, street-markets, fur- niture shops, shoe stores, hospitals, auto dealerships, etc.

One of the survey’s main principle is that each price record corresponds to a precisely defined item. That is, each price collector had detailed checklists describing

the items to be priced. For example, Cookies, brand X, box with 4 packages, 500

gr., at retail Y. This principle provides the price history of individual goods. The unbalanced panel structure draws the price history of around 65,000 items.

Importantly, we observe the same product-categories (basket) across cities but for each product-category we observe a sample of goods that might vary across regions. In order to keep the CPI representative, price collectors were advised to price the most common brands and varieties. Hence, some product categories are relatively similar across cities (e.g. soft drinks pricing Coke or Pepsi) but some other are not (e.g. women trousers).

Given confidentiality policies between Banxico and price informants, our dataset provides only few items’ characteristics. For each item we observe (i) a unique identification code per item, (ii) the region where the price was observed,

(iii) the product-category it belongs to and (iv) individual price index.4 _Products’

2_{The time window of the study is dictated mainly by major modifications in the CPI’s basket}

and methodology. Prior the 2002 base-year and after December 2010, weights, methodology and

basket of goods were updated. These breaks in the data make it difficult to compare samples for

statistical analysis.

3_{Source: www.banxico.org.mx}

4_{Product-category in our sample is similar to BLS’s ELI in the US or ONS’s COICOP classes}

details like brand, quantity/size, name or type of the outlet are not reported. In addition, we do not observe nominal prices but individual price indices assigned to each item. All individual indexes are set at 100 in the first time-wave. Subsequently, each index reflects the price variation of a particular product from one wave to the next one. Explicitly, each individual index is calculated as the ratio between the current and previous price multiplied by the index from the last wave. As one of the main objectives of this work is to evaluate the share of exchange rate fluctua-

tions passing-through consumers, working with (log) di↵erences instead of (nominal)

levels is not a limitation. Figure 3.1 depicts an example of the data available. Despite the original semi-monthly frequency reported, this research is based on monthly price changes. The majority of prices do not change every week (Kle),

and thus using semi-monthly di↵erences would result in an overwhelmingly number

of zero observations. In a similar study to ours, Aron et al. [2014] utilise 6-month

variations. In this paper, however, we consider that 6-month di↵erences omit rele-

vant exchange rate dynamics.

We now move on describing the 2 main dimensions used in our analysis, the regional and industry coverage. Regarding the first one, figure 3.2 illustrates the survey’s geographical coverage. As it can be seen from the map, observed locations are fairly distributed in the Mexican territory. The price survey provides prices from large cities, such as Monterrey, N.L. or Guadalajara, Jal. with over 3 million inhabitants, and small urban areas like Tehuantepec, Oax. or Jacona, Mich. with around 100 thousand inhabitants. Since we are interested in studying heterogeneous ERPT within the country, prices from the 46 regions are included in the analysis. These urban areas provide an excellent setup for our analysis.

With respect to the second one, the original dataset reports observations classified in 315 product-categories. Just to mention a few examples of product- categories, we observe prices of chocolate bars, women trousers, toilet paper, cinema entry, hair cut fee, etc. We neglect 76 product-categories. These categories are either associated with prices regulated by the government (e.g. gasoline or electricity fees), reported as weighted averages (e.g. accommodation services) or their prices heavily vary upon weather conditions (e.g. tomatoes). After discarding these categories, the micro-data sample comprises 239 product-categories.

An important transformation in our data is that we cluster product-categories by the North American Industry Classification System (NAICS). The matching ta- ble between product-categories and the NAICS classification is detailed in Banxico’s

CPI methodology handbook.5 Working with NAICS industry codes allows us to link

our pass-through estimates to regional and industry characteristics extracted from other national surveys. In total, we end up with 58 NAICS industries, which we would refer simply as industries form this point on.

The micro dataset used in this analysis has three main limitations for accu- rately measuring price variations. We first list the limitations and then we discuss

the potential e↵ects in our estimates.

Firstly, no variable in our micro-data flags out if the item was e↵ectively

observed or if it was out of stock. In case of the latter, the methodology at the time indicated to fill in missing values with the last observed price. Therefore, our dataset limits the number of false price variations by reporting zero price change in case the item was out of stock.

Secondly, there are no flags indicating when the item was substituted from the sample. Similarly as above, our dataset reports zero price variation. Again, it is not possible to identify when a unit was replaced but false price variations from invalid goods’ comparisons are kept to the minimum. At the time of this survey, the Bank of Mexico listed the items exiting the survey and their substitutes every month. Personal estimates point to a 1% monthly turnaround.

Thirdly, numerous studies have discussed how temporary sale prices a↵ect

studies analysing nominal frictions.6 Sale prices are not identified in the dataset.

Therefore, all prices are treated equally in our analysis (regardless if they are reg- ular or potential discount prices). Although the methodology used by Banxico for collecting the current data accepts sale prices to be included in the sample, price collectors are instructed not to report clearance sale prices as the quality of the product might have changed.

The first two issues downward bias our estimates. The reason is that we are including missing values as zero price variations in our estimates. Our economet- rical framework primarily focuses on monthly price variations and, as previously

addressed, only about 2% of our monthly observations might be a↵ected by these

measurement errors. Hence, we expect these two sources of bias to have limited impact in our estimates. The third issue upward biases our results. The intuition is that a sale price (or recovery from sale) induce a price variation not related exchange rate fluctuations. Unfortunately, without a sales indicator, it is hard to assess how many prices on average are transitory. In order not to intervene in our key depen- dent variable (magnitude of price changes), we do not utilise any kind of adjustment regarding sale prices.

6_{As documented by Kle, Nakamura and Steinsson [2008], and Mackowiak and Smets [2008], an}

important number of price changes are temporary discounts. Nevertheless, Romer [2011] argued that frequent sales could be seen as a symptom of a weak economy.

With respect to accurately measuring ERPT, our dataset presents one caveat. We are unable to identify whether an item is imported or it was produced with imported inputs. Hence, our results might not represent the most precise pass- through estimation but they can be considered as the average price response to exchange rates variations.

As treatment of outliers, we drop the 1st _{and 99}th _{percentiles of price varia-}

tions for each industry. We believe that neglecting outliers by industry is a better

approach (than the pooled 1st and 99thpercentiles) given the degree of heterogeneity

in price variation by industry.