• Publicado: 18 May 2017

  • Archivado en: Economics, Inflation, Mexico

A Dangerous New Normal

Markets on Thursday were suprised to discover Bank of Mexico increasing its key interest rate again.

The consensus in the market was a hold on the rise, but it seems Banxico probably thinks there is still a lot to do. The worrying bit is that the rise in inflation seems broad and sustained. In many cities, the average rate of inflation in the first four months of 2017 is higher than even any outliers in the same months dating back to 2013.

This is the case in 5 of 10 of the largest cities with inflation data (Monterrey, Ciudad Juarez, Leon, Torreon and Queretaro).

Largest cities

If we see the larger picture and add the rest of the smaller cities with inflation data, the trend is also worrying.

smaller cities

The dangers with this generalized new “normal” is that it essentially feeds on itself, creating a hard-to-contain upward spiral.

Basically, Banxico is right to worry.



# download
token <- "****"
d <- inflacion_ciudades(token)

dd <- melt(d, 
          id.vars = "Fechas")
dd$m <- month(dd$Fechas)
dd$period <- ifelse(dd$Fechas>'2016-12-12', "2017", "2013-2016")

large <- c("Puebla", "SanLuisPotosi", "Monterrey", "DF", 
           "CdJuarez", "Guadalajara", "Leon", "Queretaro", 
           "Veracruz", "Torreon")

dlarge <- dd %>%
  dplyr::filter(Fechas>'2012-12-12') %>%
  dplyr::filter(m %in% c(1,2,3,4)) %>%
  dplyr::filter(variable %in% large)

dsmall <- dd %>%
  dplyr::filter(Fechas>'2012-12-12') %>%
  dplyr::filter(m %in% c(1,2,3,4)) %>%
  dplyr::filter(!(variable %in% large))
         aes(x = variable, y = value))+
    geom_boxplot(color = eem_colors[8], 
                  aes( fill = period)) + 
    theme_eem() + 
    labs(x = "City", y = "Rate of Inflation", 
    title = "Comparing Rates of inflation", subtitle ="First 4 months of the year")

# repeat with other object...