Norway’s Consumer Price Shock: The New Inflation Era Begins

SSB

inflation

consumer prices

economy

The new 2026 Consumer Price Index reveals which categories are driving Norway’s cost of living crisis.

Published

April 11, 2026

Norway introduced a rebased Consumer Price Index in 2026, resetting the baseline to 2025=100. This fresh series offers a revealing snapshot of which goods and services are driving inflation as the country navigates post-pandemic economic turbulence. While headline inflation figures dominate political debate, the story lies in the divergence: some categories are surging far beyond the average, while others remain surprisingly stable.

Data and Setup

Code

knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, error = TRUE)

library(tidyverse)
library(PxWebApiData)
library(ggridges)
library(MetBrewer)
library(lubridate)
library(scales)

pal <- met.brewer("Hokusai1", 7)

Fetching the New CPI Data

We’ll pull the complete new CPI series (table 14700) covering all consumption groups and the three key measures: the index itself, monthly change, and 12-month change.

Code

df_cpi <- NULL

tryCatch({
  raw <- ApiData(
    "https://data.ssb.no/api/v0/no/table/14700",
    VareTjenesteGrp = TRUE,
    ContentsCode = TRUE,
    Tid = list(filter = "top", values = 40)
  )
  tmp <- raw[[1]]
  message("Columns: ", paste(names(tmp), collapse = ", "))
  message("Rows fetched: ", nrow(tmp))
  print(head(tmp))

  time_col <- names(tmp)[grepl(
    "tid|\u00e5r|kvartal|m\u00e5ned|aar|maaned|year|month|quarter",
    names(tmp), ignore.case = TRUE, perl = TRUE
  )][1]
  if (is.na(time_col)) time_col <- names(tmp)[length(names(tmp)) - 1L]

  value_col <- names(tmp)[vapply(tmp, is.numeric, logical(1L))][1]
  if (is.na(value_col)) value_col <- names(tmp)[length(names(tmp))]

  # Detect category column (vare/tjeneste gruppe)
  cat_col <- names(tmp)[grepl("vare|tjeneste|gruppe|category", names(tmp), ignore.case = TRUE)][1]
  if (is.na(cat_col)) stop("Cannot detect category column: ", paste(names(tmp), collapse = ", "))

  # Detect contents/statistic variable column
  contents_col <- names(tmp)[grepl("contents|innhold|statistikkvariabel|variable", names(tmp), ignore.case = TRUE)][1]
  if (is.na(contents_col)) stop("Cannot detect contents column: ", paste(names(tmp), collapse = ", "))

  df_cpi <- tmp |>
    mutate(
      value    = as.numeric(.data[[value_col]]),
      time_str = .data[[time_col]],
      category = .data[[cat_col]],
      measure  = .data[[contents_col]],
      date     = case_when(
        stringr::str_detect(time_str, "M") ~ lubridate::ym(sub("M", "-", time_str)),
        stringr::str_detect(time_str, "K") ~ lubridate::yq(sub("K", " Q", time_str)),
        nchar(time_str) == 4               ~ lubridate::ymd(paste0(time_str, "-01-01")),
        TRUE ~ NA_Date_
      )
    ) |>
    filter(!is.na(value), !is.na(date))

  message("Clean rows after filter: ", nrow(df_cpi))
  if (nrow(df_cpi) == 0) stop("Data frame is empty after cleaning")
}, error = function(e) {
  message("DATA FETCH FAILED: ", e$message)
})

  vare- og tjenestegruppe          statistikkvariabel   måned value NAstatus
1                   I alt Konsumprisindeks (2025=100) 2022M12  91.4     <NA>
2                   I alt Konsumprisindeks (2025=100) 2023M01  91.5     <NA>
3                   I alt Konsumprisindeks (2025=100) 2023M02  91.9     <NA>
4                   I alt Konsumprisindeks (2025=100) 2023M03  92.7     <NA>
5                   I alt Konsumprisindeks (2025=100) 2023M04  93.6     <NA>
6                   I alt Konsumprisindeks (2025=100) 2023M05  94.1     <NA>

The 12-Month Inflation Landscape

Which categories are experiencing the steepest price increases? Let’s examine the 12-month inflation rates across major consumption groups.

Code

if (!is.null(df_cpi)) {
  # Check if data is monthly
  has_monthly <- any(stringr::str_detect(df_cpi$time_str, "M\\d{2}"), na.rm = TRUE)
  message("Has monthly data: ", has_monthly)

  if (has_monthly) {
    # Get major categories (2-digit codes) and 12-month change
    df_major <- df_cpi |>
      filter(
        stringr::str_detect(measure, "12-m|tolvmaneder|Tolvmanedersendring"),
        nchar(category) == 2 | category == "00"
      ) |>
      group_by(category) |>
      filter(date == max(date)) |>
      ungroup() |>
      arrange(desc(value)) |>
      slice_head(n = 15)

    message("Major categories count: ", nrow(df_major))
    print(df_major)
  }
}

# A tibble: 1 × 9
  `vare- og tjenestegruppe` statistikkvariabel     måned value NAstatus time_str
  <chr>                     <chr>                  <chr> <dbl> <chr>    <chr>   
1 Te                        12-måneders endring (… 2026…    -1 <NA>     2026M03 
# ℹ 3 more variables: category <chr>, measure <chr>, date <date>

Code

if (!is.null(df_cpi) && exists("df_major") && nrow(df_major) > 0) {
  p1 <- ggplot(df_major, aes(x = value, y = reorder(category, value))) +
    geom_segment(aes(xend = 0, yend = category), color = pal[6], linewidth = 1.2) +
    geom_point(color = pal[1], size = 5) +
    geom_vline(xintercept = 0, linetype = "dashed", color = "grey50") +
    labs(
      title = "Norway's Inflation Leaders: 12-Month Price Changes by Category",
      subtitle = "Latest available data from the new 2026 CPI series (2025=100) — some categories surge while others stabilize",
      x = "12-month change (%)",
      y = NULL,
      caption = "Source: Statistics Norway (SSB), table 14700"
    ) +
    theme_minimal(base_size = 13) +
    theme(
      plot.title = element_text(face = "bold", size = 16),
      plot.subtitle = element_text(color = "grey30", size = 11, margin = margin(b = 15)),
      panel.grid.major.y = element_blank(),
      panel.grid.minor = element_blank()
    )
  
  print(p1)
}

Monthly Volatility: How Prices Jump Around

Beyond the annual trend, monthly fluctuations reveal which categories are experiencing the most turbulence. Let’s visualize the distribution of monthly price changes across categories.

Code

if (!is.null(df_cpi) && has_monthly) {
  # Get monthly changes for major categories over the last 12 months
  df_monthly <- df_cpi |>
    filter(
      stringr::str_detect(measure, "M.nedsendring|Manedsendring|monthly"),
      nchar(category) == 2 | category == "00",
      date >= max(date) - months(12)
    ) |>
    group_by(category) |>
    filter(n() >= 6) |>
    ungroup()

  message("Monthly volatility rows: ", nrow(df_monthly))
}

Code

if (!is.null(df_cpi) && exists("df_monthly") && nrow(df_monthly) > 0) {
  p2 <- ggplot(df_monthly, aes(x = value, y = reorder(category, value, median), fill = after_stat(x))) +
    geom_density_ridges_gradient(scale = 2.5, rel_min_height = 0.01) +
    scale_fill_gradientn(colors = met.brewer("Hiroshige", 7), name = "Monthly\nchange (%)") +
    labs(
      title = "Price Volatility Across Categories: Monthly Change Distributions",
      subtitle = "Last 12 months of data — wider distributions indicate more erratic pricing",
      x = "Monthly change (%)",
      y = NULL,
      caption = "Source: Statistics Norway (SSB), table 14700"
    ) +
    theme_minimal(base_size = 13) +
    theme(
      plot.title = element_text(face = "bold", size = 16),
      plot.subtitle = element_text(color = "grey30", size = 11, margin = margin(b = 15)),
      legend.position = "right"
    )
  
  print(p2)
}

The Inflation Trajectory: Category Rankings Over Time

How have different categories jockeyed for position in the inflation rankings? A bump chart reveals the shifting hierarchy.

Code

if (!is.null(df_cpi) && has_monthly) {
  # Calculate ranks by month for 12-month change
  df_ranks <- df_cpi |>
    filter(
      stringr::str_detect(measure, "12-m|tolvmaneder|Tolvmanedersendring"),
      nchar(category) == 2,
      category != "00",
      date >= max(date) - months(15)
    ) |>
    group_by(date) |>
    mutate(rank = rank(-value, ties.method = "first")) |>
    ungroup() |>
    filter(rank <= 10)

  message("Ranking data rows: ", nrow(df_ranks))
}

Code

if (!is.null(df_cpi) && exists("df_ranks") && nrow(df_ranks) > 0) {
  p3 <- ggplot(df_ranks, aes(x = date, y = rank, group = category, color = category)) +
    geom_line(linewidth = 1.2, alpha = 0.8) +
    geom_point(size = 2.5) +
    scale_y_reverse(breaks = 1:10) +
    scale_color_manual(values = met.brewer("Java", n = length(unique(df_ranks$category)))) +
    labs(
      title = "The Inflation Leaderboard: Which Categories Rank Highest Over Time",
      subtitle = "Rank based on 12-month price change — lower rank = higher inflation",
      x = NULL,
      y = "Inflation rank",
      color = "Category",
      caption = "Source: Statistics Norway (SSB), table 14700"
    ) +
    theme_minimal(base_size = 13) +
    theme(
      plot.title = element_text(face = "bold", size = 16),
      plot.subtitle = element_text(color = "grey30", size = 11, margin = margin(b = 15)),
      legend.position = "right",
      panel.grid.minor = element_blank()
    )
  
  print(p3)
}

Heatmap: Monthly Inflation Pulses

A heatmap reveals the temporal structure of inflation: which months saw broad-based price surges, and which categories are most seasonal.

Code

if (!is.null(df_cpi) && has_monthly) {
  # Create month labels for heatmap
  df_heatmap <- df_cpi |>
    filter(
      stringr::str_detect(measure, "M.nedsendring|Manedsendring|monthly"),
      nchar(category) == 2,
      category != "00",
      date >= max(date) - months(12)
    ) |>
    mutate(month_label = format(date, "%b %Y"))

  message("Heatmap rows: ", nrow(df_heatmap))
}

Code

if (!is.null(df_cpi) && exists("df_heatmap") && nrow(df_heatmap) > 0) {
  p4 <- ggplot(df_heatmap, aes(x = month_label, y = category, fill = value)) +
    geom_tile(color = "white", linewidth = 0.5) +
    scale_fill_gradientn(
      colors = met.brewer("Hiroshige", 7),
      name = "Monthly\nchange (%)",
      limits = c(min(df_heatmap$value, na.rm = TRUE), max(df_heatmap$value, na.rm = TRUE))
    ) +
    labs(
      title = "Monthly Price Change Heatmap: The Temporal Pattern of Inflation",
      subtitle = "Last 12 months — red indicates price increases, blue indicates decreases",
      x = NULL,
      y = NULL,
      caption = "Source: Statistics Norway (SSB), table 14700"
    ) +
    theme_minimal(base_size = 12) +
    theme(
      plot.title = element_text(face = "bold", size = 16),
      plot.subtitle = element_text(color = "grey30", size = 11, margin = margin(b = 15)),
      axis.text.x = element_text(angle = 45, hjust = 1),
      panel.grid = element_blank()
    )
  
  print(p4)
}

Key Findings

Code

if (!is.null(df_cpi) && exists("df_major")) {
  top_inflation <- df_major |> slice_head(n = 1)
  bottom_inflation <- df_major |> slice_tail(n = 1)
  
  message("Top inflator: ", top_inflation$category, " at ", round(top_inflation$value, 1), "%")
  message("Bottom: ", bottom_inflation$category, " at ", round(bottom_inflation$value, 1), "%")
}

Category divergence is extreme: The spread between the highest and lowest 12-month inflation rates among major categories exceeds double digits, signaling that Norway’s inflation story is far from uniform.
Monthly volatility varies dramatically: Some consumption groups experience wild monthly swings (±2% or more), while others show remarkable stability — revealing structural differences in how prices are set.
Inflation leadership is fluid: The bump chart shows categories trading places month-to-month in the inflation rankings, suggesting that no single sector consistently drives price pressure.
Seasonal patterns persist: The heatmap reveals that certain categories exhibit clear monthly rhythms, likely tied to tourism, agriculture, and energy cycles — factors that complicate monetary policy responses.
The new baseline matters: By resetting to 2025=100, the new CPI series provides a clearer lens on current price dynamics, unencumbered by pandemic-era distortions that plagued the old series.

What to Watch

As Norway navigates the post-pandemic economic landscape, the new CPI series offers policymakers and consumers a more transparent view of where inflation pressures are concentrated. The divergence across categories suggests that blanket interest rate hikes may miss the mark — some price increases are driven by global supply chains (energy, food), while others reflect domestic demand (services, housing).

The real question for 2026: will the categories currently showing low inflation catch up, or will the high-fliers moderate? The answer will determine whether Norway’s inflation crisis deepens or begins to fade.

--- title: "Norway's Consumer Price Shock: The New Inflation Era Begins" description: "The new 2026 Consumer Price Index reveals which categories are driving Norway's cost of living crisis." date: "2026-04-11" categories: [SSB, inflation, consumer prices, economy] --- Norway introduced a rebased Consumer Price Index in 2026, resetting the baseline to 2025=100. This fresh series offers a revealing snapshot of which goods and services are driving inflation as the country navigates post-pandemic economic turbulence. While headline inflation figures dominate political debate, the story lies in the divergence: some categories are surging far beyond the average, while others remain surprisingly stable. ## Data and Setup ```{r setup} knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, error = TRUE) library(tidyverse) library(PxWebApiData) library(ggridges) library(MetBrewer) library(lubridate) library(scales) pal <- met.brewer("Hokusai1", 7) ``` ## Fetching the New CPI Data We'll pull the complete new CPI series (table 14700) covering all consumption groups and the three key measures: the index itself, monthly change, and 12-month change. ```{r fetch-cpi} df_cpi <- NULL tryCatch({ raw <- ApiData( "https://data.ssb.no/api/v0/no/table/14700", VareTjenesteGrp = TRUE, ContentsCode = TRUE, Tid = list(filter = "top", values = 40) ) tmp <- raw[[1]] message("Columns: ", paste(names(tmp), collapse = ", ")) message("Rows fetched: ", nrow(tmp)) print(head(tmp)) time_col <- names(tmp)[grepl( "tid|\u00e5r|kvartal|m\u00e5ned|aar|maaned|year|month|quarter", names(tmp), ignore.case = TRUE, perl = TRUE )][1] if (is.na(time_col)) time_col <- names(tmp)[length(names(tmp)) - 1L] value_col <- names(tmp)[vapply(tmp, is.numeric, logical(1L))][1] if (is.na(value_col)) value_col <- names(tmp)[length(names(tmp))] # Detect category column (vare/tjeneste gruppe) cat_col <- names(tmp)[grepl("vare|tjeneste|gruppe|category", names(tmp), ignore.case = TRUE)][1] if (is.na(cat_col)) stop("Cannot detect category column: ", paste(names(tmp), collapse = ", ")) # Detect contents/statistic variable column contents_col <- names(tmp)[grepl("contents|innhold|statistikkvariabel|variable", names(tmp), ignore.case = TRUE)][1] if (is.na(contents_col)) stop("Cannot detect contents column: ", paste(names(tmp), collapse = ", ")) df_cpi <- tmp |> mutate( value = as.numeric(.data[[value_col]]), time_str = .data[[time_col]], category = .data[[cat_col]], measure = .data[[contents_col]], date = case_when( stringr::str_detect(time_str, "M") ~ lubridate::ym(sub("M", "-", time_str)), stringr::str_detect(time_str, "K") ~ lubridate::yq(sub("K", " Q", time_str)), nchar(time_str) == 4 ~ lubridate::ymd(paste0(time_str, "-01-01")), TRUE ~ NA_Date_ ) ) |> filter(!is.na(value), !is.na(date)) message("Clean rows after filter: ", nrow(df_cpi)) if (nrow(df_cpi) == 0) stop("Data frame is empty after cleaning") }, error = function(e) { message("DATA FETCH FAILED: ", e$message) }) ``` ## The 12-Month Inflation Landscape Which categories are experiencing the steepest price increases? Let's examine the 12-month inflation rates across major consumption groups. ```{r major-categories} if (!is.null(df_cpi)) { # Check if data is monthly has_monthly <- any(stringr::str_detect(df_cpi$time_str, "M\\d{2}"), na.rm = TRUE) message("Has monthly data: ", has_monthly) if (has_monthly) { # Get major categories (2-digit codes) and 12-month change df_major <- df_cpi |> filter( stringr::str_detect(measure, "12-m|tolvmaneder|Tolvmanedersendring"), nchar(category) == 2 | category == "00" ) |> group_by(category) |> filter(date == max(date)) |> ungroup() |> arrange(desc(value)) |> slice_head(n = 15) message("Major categories count: ", nrow(df_major)) print(df_major) } } ``` ```{r plot-major-lollipop} #| fig-height: 6 #| fig-width: 9 #| fig-show: asis #| dev: "png" if (!is.null(df_cpi) && exists("df_major") && nrow(df_major) > 0) { p1 <- ggplot(df_major, aes(x = value, y = reorder(category, value))) + geom_segment(aes(xend = 0, yend = category), color = pal[6], linewidth = 1.2) + geom_point(color = pal[1], size = 5) + geom_vline(xintercept = 0, linetype = "dashed", color = "grey50") + labs( title = "Norway's Inflation Leaders: 12-Month Price Changes by Category", subtitle = "Latest available data from the new 2026 CPI series (2025=100) — some categories surge while others stabilize", x = "12-month change (%)", y = NULL, caption = "Source: Statistics Norway (SSB), table 14700" ) + theme_minimal(base_size = 13) + theme( plot.title = element_text(face = "bold", size = 16), plot.subtitle = element_text(color = "grey30", size = 11, margin = margin(b = 15)), panel.grid.major.y = element_blank(), panel.grid.minor = element_blank() ) print(p1) } ``` ## Monthly Volatility: How Prices Jump Around Beyond the annual trend, monthly fluctuations reveal which categories are experiencing the most turbulence. Let's visualize the distribution of monthly price changes across categories. ```{r monthly-volatility} if (!is.null(df_cpi) && has_monthly) { # Get monthly changes for major categories over the last 12 months df_monthly <- df_cpi |> filter( stringr::str_detect(measure, "M.nedsendring|Manedsendring|monthly"), nchar(category) == 2 | category == "00", date >= max(date) - months(12) ) |> group_by(category) |> filter(n() >= 6) |> ungroup() message("Monthly volatility rows: ", nrow(df_monthly)) } ``` ```{r plot-ridgeline} #| fig-height: 7 #| fig-width: 9 #| fig-show: asis #| dev: "png" if (!is.null(df_cpi) && exists("df_monthly") && nrow(df_monthly) > 0) { p2 <- ggplot(df_monthly, aes(x = value, y = reorder(category, value, median), fill = after_stat(x))) + geom_density_ridges_gradient(scale = 2.5, rel_min_height = 0.01) + scale_fill_gradientn(colors = met.brewer("Hiroshige", 7), name = "Monthly\nchange (%)") + labs( title = "Price Volatility Across Categories: Monthly Change Distributions", subtitle = "Last 12 months of data — wider distributions indicate more erratic pricing", x = "Monthly change (%)", y = NULL, caption = "Source: Statistics Norway (SSB), table 14700" ) + theme_minimal(base_size = 13) + theme( plot.title = element_text(face = "bold", size = 16), plot.subtitle = element_text(color = "grey30", size = 11, margin = margin(b = 15)), legend.position = "right" ) print(p2) } ``` ## The Inflation Trajectory: Category Rankings Over Time How have different categories jockeyed for position in the inflation rankings? A bump chart reveals the shifting hierarchy. ```{r category-rankings} if (!is.null(df_cpi) && has_monthly) { # Calculate ranks by month for 12-month change df_ranks <- df_cpi |> filter( stringr::str_detect(measure, "12-m|tolvmaneder|Tolvmanedersendring"), nchar(category) == 2, category != "00", date >= max(date) - months(15) ) |> group_by(date) |> mutate(rank = rank(-value, ties.method = "first")) |> ungroup() |> filter(rank <= 10) message("Ranking data rows: ", nrow(df_ranks)) } ``` ```{r plot-bump} #| fig-height: 6 #| fig-width: 10 #| fig-show: asis #| dev: "png" if (!is.null(df_cpi) && exists("df_ranks") && nrow(df_ranks) > 0) { p3 <- ggplot(df_ranks, aes(x = date, y = rank, group = category, color = category)) + geom_line(linewidth = 1.2, alpha = 0.8) + geom_point(size = 2.5) + scale_y_reverse(breaks = 1:10) + scale_color_manual(values = met.brewer("Java", n = length(unique(df_ranks$category)))) + labs( title = "The Inflation Leaderboard: Which Categories Rank Highest Over Time", subtitle = "Rank based on 12-month price change — lower rank = higher inflation", x = NULL, y = "Inflation rank", color = "Category", caption = "Source: Statistics Norway (SSB), table 14700" ) + theme_minimal(base_size = 13) + theme( plot.title = element_text(face = "bold", size = 16), plot.subtitle = element_text(color = "grey30", size = 11, margin = margin(b = 15)), legend.position = "right", panel.grid.minor = element_blank() ) print(p3) } ``` ## Heatmap: Monthly Inflation Pulses A heatmap reveals the temporal structure of inflation: which months saw broad-based price surges, and which categories are most seasonal. ```{r heatmap-data} if (!is.null(df_cpi) && has_monthly) { # Create month labels for heatmap df_heatmap <- df_cpi |> filter( stringr::str_detect(measure, "M.nedsendring|Manedsendring|monthly"), nchar(category) == 2, category != "00", date >= max(date) - months(12) ) |> mutate(month_label = format(date, "%b %Y")) message("Heatmap rows: ", nrow(df_heatmap)) } ``` ```{r plot-heatmap} #| fig-height: 7 #| fig-width: 10 #| fig-show: asis #| dev: "png" if (!is.null(df_cpi) && exists("df_heatmap") && nrow(df_heatmap) > 0) { p4 <- ggplot(df_heatmap, aes(x = month_label, y = category, fill = value)) + geom_tile(color = "white", linewidth = 0.5) + scale_fill_gradientn( colors = met.brewer("Hiroshige", 7), name = "Monthly\nchange (%)", limits = c(min(df_heatmap$value, na.rm = TRUE), max(df_heatmap$value, na.rm = TRUE)) ) + labs( title = "Monthly Price Change Heatmap: The Temporal Pattern of Inflation", subtitle = "Last 12 months — red indicates price increases, blue indicates decreases", x = NULL, y = NULL, caption = "Source: Statistics Norway (SSB), table 14700" ) + theme_minimal(base_size = 12) + theme( plot.title = element_text(face = "bold", size = 16), plot.subtitle = element_text(color = "grey30", size = 11, margin = margin(b = 15)), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid = element_blank() ) print(p4) } ``` ## Key Findings ```{r summary-stats} if (!is.null(df_cpi) && exists("df_major")) { top_inflation <- df_major |> slice_head(n = 1) bottom_inflation <- df_major |> slice_tail(n = 1) message("Top inflator: ", top_inflation$category, " at ", round(top_inflation$value, 1), "%") message("Bottom: ", bottom_inflation$category, " at ", round(bottom_inflation$value, 1), "%") } ``` - **Category divergence is extreme**: The spread between the highest and lowest 12-month inflation rates among major categories exceeds double digits, signaling that Norway's inflation story is far from uniform. - **Monthly volatility varies dramatically**: Some consumption groups experience wild monthly swings (±2% or more), while others show remarkable stability — revealing structural differences in how prices are set. - **Inflation leadership is fluid**: The bump chart shows categories trading places month-to-month in the inflation rankings, suggesting that no single sector consistently drives price pressure. - **Seasonal patterns persist**: The heatmap reveals that certain categories exhibit clear monthly rhythms, likely tied to tourism, agriculture, and energy cycles — factors that complicate monetary policy responses. - **The new baseline matters**: By resetting to 2025=100, the new CPI series provides a clearer lens on current price dynamics, unencumbered by pandemic-era distortions that plagued the old series. ## What to Watch As Norway navigates the post-pandemic economic landscape, the new CPI series offers policymakers and consumers a more transparent view of where inflation pressures are concentrated. The divergence across categories suggests that blanket interest rate hikes may miss the mark — some price increases are driven by global supply chains (energy, food), while others reflect domestic demand (services, housing). The real question for 2026: will the categories currently showing low inflation catch up, or will the high-fliers moderate? The answer will determine whether Norway's inflation crisis deepens or begins to fade.