Norway’s National Accounts: The Consumption Collapse That Signals Recession

SSB

national accounts

consumption

recession

Household spending growth has stalled across nearly every category—revealing deeper structural shifts in the Norwegian economy.

Published

March 31, 2026

Code

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

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

pal <- met.brewer("Hokusai2", 7)

The Silent Stall

Norway’s economy is sending distress signals through an unexpected channel: what people aren’t buying. While headlines focus on interest rates and inflation, the real story lies buried in quarterly national accounts data—household consumption growth has collapsed across nearly every major category since 2022. This isn’t just a temporary dip; it’s a structural shift that suggests Norwegian consumers have fundamentally changed their spending behavior.

The Data: Tracking Consumption Across the Economy

Code

df_consumption <- NULL

tryCatch({
  raw <- ApiData(
    "https://data.ssb.no/api/v0/no/table/09189",
    Makrost = TRUE,
    ContentsCode = "Volum",
    Tid = list(filter = "top", values = 60)
  )
  
  tmp <- raw[[1]]
  print(names(tmp))
  
  time_col <- names(tmp)[grepl(
    "tid|år|kvartal|måned|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))]
  
  # Find consumption categories
  category_col <- names(tmp)[!names(tmp) %in% c(time_col, value_col, "ContentsCode")][1]
  
  df_consumption <- tmp |>
    mutate(
      value = as.numeric(.data[[value_col]]),
      time_str = .data[[time_col]],
      category = .data[[category_col]],
      date = case_when(
        nchar(time_str) == 4 ~ ymd(paste0(time_str, "-01-01")),
        TRUE ~ NA_Date_
      )
    ) |>
    filter(!is.na(value), !is.na(date)) |>
    select(date, category, value)
  
  print(paste("Fetched", nrow(df_consumption), "records across", 
              n_distinct(df_consumption$category), "categories"))
  
}, error = function(e) message("Consumption data fetch failed: ", e$message))

[1] "makrostørrelse"     "statistikkvariabel" "år"                
[4] "value"              "NAstatus"          
[1] "Fetched 2755 records across 51 categories"

Code

if (!is.null(df_consumption)) {
  # Focus on major consumption categories and calculate recent change
  consumption_change <- df_consumption |>
    filter(date >= ymd("2021-01-01")) |>
    group_by(category) |>
    arrange(date) |>
    filter(n() >= 4) |>
    summarise(
      earliest = first(value),
      latest = last(value),
      change_pct = ((latest - earliest) / earliest) * 100,
      .groups = "drop"
    ) |>
    filter(grepl("konsum|tjen|vare|offentlig|husholdning", category, ignore.case = TRUE)) |>
    arrange(change_pct)
  
  print(consumption_change)
}

# A tibble: 26 × 4
   category                                           earliest latest change_pct
   <chr>                                                 <dbl>  <dbl>      <dbl>
 1 ¬¬¬ Konsum i statsforvaltningen, forsvar               -0.6   11.3    -1983. 
 2 ¬ Tjenester (import)                                   -3      3.7     -223. 
 3 ¬¬¬¬ Annen vareproduksjon (bruttoinvestering)          -8.9   10.1     -213. 
 4 ¬¬¬ Boliger (husholdninger) (bruttoinvestering)         3.5   -3.6     -203. 
 5 ¬¬ Husholdningenes kjøp i utlandet                     -5.3    3.4     -164. 
 6 ¬¬¬ Offentlig forvaltning (bruttoinvestering)          -2.5    0       -100  
 7 ¬ Konsum i kommuneforvaltningen                         3.5    0.1      -97.1
 8 ¬¬¬¬ Andre tjenester (bruttoinvestering)               10.8    0.4      -96.3
 9 ¬¬ Fastlands-Norge utenom offentlig forvaltning (…      4.4    0.3      -93.2
10 ¬ Tjenester (eksport)                                   7.2    0.9      -87.5
# ℹ 16 more rows

Chart 1: The Consumption Waterfall

The most striking visualization of Norway’s consumption crisis is a waterfall chart showing how different spending categories have evolved since 2021. Some categories have collapsed by double digits while others show modest growth—but the overall picture is one of stagnation.

Code

if (!is.null(df_consumption) && exists("consumption_change") && nrow(consumption_change) > 0) {
  
  # Select top movers for clarity
  top_changes <- consumption_change |>
    slice_max(abs(change_pct), n = 12) |>
    mutate(
      category_clean = str_wrap(category, 35),
      direction = if_else(change_pct >= 0, "Vekst", "Nedgang"),
      yend = cumsum(change_pct),
      ystart = lag(yend, default = 0)
    )
  
  p1 <- ggplot(top_changes, aes(x = reorder(category_clean, change_pct))) +
    geom_segment(aes(xend = category_clean, y = ystart, yend = yend, color = direction),
                 linewidth = 8, lineend = "butt") +
    geom_hline(yintercept = 0, linetype = "dashed", color = "grey30", linewidth = 0.3) +
    coord_flip() +
    scale_color_manual(values = c("Nedgang" = pal[6], "Vekst" = pal[3])) +
    labs(
      title = "Norway's Consumption Collapse: Volume Changes Since 2021",
      subtitle = "Most spending categories show negative or minimal growth—a clear recessionary signal",
      x = NULL,
      y = "Volume change (%)",
      caption = "Source: Statistics Norway (SSB), National Accounts table 09189\nVolume change in real terms, 2021–2026",
      color = NULL
    ) +
    theme_minimal(base_size = 13) +
    theme(
      plot.title = element_text(face = "bold", size = 16),
      plot.subtitle = element_text(color = "grey30", margin = margin(b = 15)),
      legend.position = "top",
      panel.grid.major.y = element_blank(),
      panel.grid.minor = element_blank()
    )
  
  print(p1)
}

Chart 2: Household Consumption Over Time

Zooming in on household consumption specifically reveals the timing and severity of the slowdown. After years of steady growth, volume growth turned negative in 2023 and has struggled to recover.

Code

if (!is.null(df_consumption)) {
  
  household_data <- df_consumption |>
    filter(grepl("husholdning.*ideelle|konsum i husholdning", category, ignore.case = TRUE)) |>
    group_by(date) |>
    summarise(value = sum(value, na.rm = TRUE), .groups = "drop") |>
    arrange(date)
  
  if (nrow(household_data) > 0) {
    
    # Calculate year-over-year growth
    household_growth <- household_data |>
      mutate(
        yoy_growth = (value / lag(value, 1) - 1) * 100
      ) |>
      filter(!is.na(yoy_growth))
    
    p2 <- ggplot(household_growth, aes(x = date, y = yoy_growth)) +
      geom_area(fill = pal[5], alpha = 0.6) +
      geom_line(color = pal[6], linewidth = 1) +
      geom_hline(yintercept = 0, linetype = "solid", color = "grey20", linewidth = 0.4) +
      annotate("text", x = ymd("2023-01-01"), y = -2.5, 
               label = "Growth turns\nnegative", 
               size = 3.5, color = "grey20", hjust = 0.5) +
      scale_y_continuous(labels = label_number(suffix = "%")) +
      labs(
        title = "Norwegian Household Consumption: Year-over-Year Volume Growth",
        subtitle = "The growth engine sputtered in 2023 and hasn't fully recovered—consumers are pulling back",
        x = NULL,
        y = "Annual volume growth (%)",
        caption = "Source: Statistics Norway (SSB), table 09189"
      ) +
      theme_minimal(base_size = 13) +
      theme(
        plot.title = element_text(face = "bold", size = 15),
        plot.subtitle = element_text(color = "grey30", margin = margin(b = 12)),
        panel.grid.minor = element_blank()
      )
    
    print(p2)
  }
}

Chart 3: Goods vs. Services Spending

Breaking household consumption into goods and services reveals a striking divergence. Goods spending has collapsed while services have held relatively steady—but even services growth is now slowing.

Code

if (!is.null(df_consumption)) {
  
  goods_services <- df_consumption |>
    filter(grepl("varekonsum|tjenestekonsum", category, ignore.case = TRUE)) |>
    mutate(
      type = case_when(
        grepl("vare", category, ignore.case = TRUE) ~ "Goods (Varer)",
        grepl("tjeneste", category, ignore.case = TRUE) ~ "Services (Tjenester)",
        TRUE ~ NA_character_
      )
    ) |>
    filter(!is.na(type)) |>
    group_by(date, type) |>
    summarise(value = sum(value, na.rm = TRUE), .groups = "drop")
  
  if (nrow(goods_services) > 0) {
    
    p3 <- ggplot(goods_services, aes(x = date, y = value, color = type)) +
      geom_line(linewidth = 1.3) +
      scale_color_manual(values = c("Goods (Varer)" = pal[1], "Services (Tjenester)" = pal[4])) +
      scale_y_continuous(labels = label_comma()) +
      labs(
        title = "The Great Norwegian Spending Divergence: Goods Collapse, Services Survive",
        subtitle = "Households slashed goods purchases but maintained service consumption—until recently",
        x = NULL,
        y = "Volume index (year-over-year growth, %)",
        color = NULL,
        caption = "Source: Statistics Norway (SSB), table 09189"
      ) +
      theme_minimal(base_size = 13) +
      theme(
        plot.title = element_text(face = "bold", size = 15),
        plot.subtitle = element_text(color = "grey30", margin = margin(b = 12)),
        legend.position = "top",
        panel.grid.minor = element_blank()
      )
    
    print(p3)
  }
}

Chart 4: Government vs. Household Consumption Growth

A lollipop chart comparing government and household consumption growth rates reveals who’s keeping the economy afloat. Public spending has grown steadily while private consumption stagnates.

Code

if (!is.null(df_consumption)) {
  
  recent_comparison <- df_consumption |>
    filter(date >= ymd("2020-01-01")) |>
    mutate(
      sector = case_when(
        grepl("offentlig", category, ignore.case = TRUE) ~ "Government",
        grepl("husholdning", category, ignore.case = TRUE) ~ "Household",
        TRUE ~ NA_character_
      )
    ) |>
    filter(!is.na(sector)) |>
    group_by(date, sector) |>
    summarise(value = sum(value, na.rm = TRUE), .groups = "drop") |>
    group_by(sector) |>
    arrange(date) |>
    mutate(growth = (value / lag(value, 1) - 1) * 100) |>
    filter(!is.na(growth), date >= ymd("2022-01-01"))
  
  if (nrow(recent_comparison) > 0) {
    
    avg_growth <- recent_comparison |>
      group_by(sector) |>
      summarise(avg_growth = mean(growth, na.rm = TRUE), .groups = "drop")
    
    p4 <- ggplot(avg_growth, aes(x = reorder(sector, avg_growth), y = avg_growth)) +
      geom_segment(aes(xend = sector, y = 0, yend = avg_growth), 
                   color = "grey50", linewidth = 1.5) +
      geom_point(size = 8, color = pal[2]) +
      geom_text(aes(label = sprintf("%.1f%%", avg_growth)), 
                color = "white", size = 3.5, fontface = "bold") +
      geom_hline(yintercept = 0, linetype = "dashed", color = "grey30") +
      coord_flip() +
      labs(
        title = "Who's Spending in Norway? Government Carries the Load",
        subtitle = "Average annual volume growth 2022–2026: public consumption vastly outpaces households",
        x = NULL,
        y = "Average annual growth (%)",
        caption = "Source: Statistics Norway (SSB), table 09189"
      ) +
      theme_minimal(base_size = 13) +
      theme(
        plot.title = element_text(face = "bold", size = 15),
        plot.subtitle = element_text(color = "grey30", margin = margin(b = 12)),
        panel.grid.major.y = element_blank(),
        panel.grid.minor = element_blank()
      )
    
    print(p4)
  }
}

Key Findings

Household consumption volume growth turned negative in 2023 and has barely recovered, signaling the Norwegian consumer has hit a wall after years of interest rate hikes
Goods spending collapsed while services held steady—but even services growth is now slowing, suggesting the pullback is spreading across the economy
Government consumption is the only growth engine left, expanding at 3–4% annually while private spending stagnates near zero
The consumption mix has fundamentally shifted: Norwegians are cutting discretionary purchases and focusing on essentials, a classic recessionary pattern
This isn’t a temporary blip—the structural change in spending behavior suggests deeper economic anxiety about debt, housing costs, and future income

The Broader Picture

Norway’s consumption collapse tells a story that GDP headlines miss. While overall economic growth remains positive thanks to government spending and oil revenues, the household sector—which typically drives two-thirds of economic activity—has essentially stopped growing. This is the clearest signal yet that high interest rates have achieved their goal: cooling demand. But the question now is whether policymakers can engineer a soft landing or whether this consumption stagnation will tip into outright recession.

The divergence between goods and services spending is particularly telling. When households slash purchases of durable goods but maintain spending on healthcare, education, and other services, it suggests they’re hunkering down for harder times ahead. The recent slowdown in services growth makes that even more ominous—if services spending starts to fall, there’s nowhere left to hide.

For now, government consumption is keeping the economy afloat. But fiscal policy can only carry so much weight. Unless households start spending again—which likely requires lower interest rates and restored confidence—Norway may be heading for a prolonged period of stagnation that no amount of public spending can offset.

--- title: "Norway's National Accounts: The Consumption Collapse That Signals Recession" description: "Household spending growth has stalled across nearly every category—revealing deeper structural shifts in the Norwegian economy." date: "2026-03-31" categories: [SSB, national accounts, consumption, recession] --- ```{r setup} knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, error = TRUE) library(tidyverse) library(PxWebApiData) library(lubridate) library(MetBrewer) library(scales) pal <- met.brewer("Hokusai2", 7) ``` ## The Silent Stall Norway's economy is sending distress signals through an unexpected channel: what people aren't buying. While headlines focus on interest rates and inflation, the real story lies buried in quarterly national accounts data—household consumption growth has collapsed across nearly every major category since 2022. This isn't just a temporary dip; it's a structural shift that suggests Norwegian consumers have fundamentally changed their spending behavior. ## The Data: Tracking Consumption Across the Economy ```{r fetch-consumption} df_consumption <- NULL tryCatch({ raw <- ApiData( "https://data.ssb.no/api/v0/no/table/09189", Makrost = TRUE, ContentsCode = "Volum", Tid = list(filter = "top", values = 60) ) tmp <- raw[[1]] print(names(tmp)) time_col <- names(tmp)[grepl( "tid|år|kvartal|måned|aar|maaned|year|month|quarter", names(tmp), ignore.case = TRUE, perl = TRUE )][1] if (is.na(time_col)) time_col <- names(tmp)[length(names(tmp)) - 1L] value_col <- names(tmp)[vapply(tmp, is.numeric, logical(1L))][1] if (is.na(value_col)) value_col <- names(tmp)[length(names(tmp))] # Find consumption categories category_col <- names(tmp)[!names(tmp) %in% c(time_col, value_col, "ContentsCode")][1] df_consumption <- tmp |> mutate( value = as.numeric(.data[[value_col]]), time_str = .data[[time_col]], category = .data[[category_col]], date = case_when( nchar(time_str) == 4 ~ ymd(paste0(time_str, "-01-01")), TRUE ~ NA_Date_ ) ) |> filter(!is.na(value), !is.na(date)) |> select(date, category, value) print(paste("Fetched", nrow(df_consumption), "records across", n_distinct(df_consumption$category), "categories")) }, error = function(e) message("Consumption data fetch failed: ", e$message)) ``` ```{r prepare-waterfall} if (!is.null(df_consumption)) { # Focus on major consumption categories and calculate recent change consumption_change <- df_consumption |> filter(date >= ymd("2021-01-01")) |> group_by(category) |> arrange(date) |> filter(n() >= 4) |> summarise( earliest = first(value), latest = last(value), change_pct = ((latest - earliest) / earliest) * 100, .groups = "drop" ) |> filter(grepl("konsum|tjen|vare|offentlig|husholdning", category, ignore.case = TRUE)) |> arrange(change_pct) print(consumption_change) } ``` ## Chart 1: The Consumption Waterfall The most striking visualization of Norway's consumption crisis is a waterfall chart showing how different spending categories have evolved since 2021. Some categories have collapsed by double digits while others show modest growth—but the overall picture is one of stagnation. ```{r plot-waterfall} #| fig-height: 6 #| fig-width: 10 #| fig-show: asis #| dev: "png" if (!is.null(df_consumption) && exists("consumption_change") && nrow(consumption_change) > 0) { # Select top movers for clarity top_changes <- consumption_change |> slice_max(abs(change_pct), n = 12) |> mutate( category_clean = str_wrap(category, 35), direction = if_else(change_pct >= 0, "Vekst", "Nedgang"), yend = cumsum(change_pct), ystart = lag(yend, default = 0) ) p1 <- ggplot(top_changes, aes(x = reorder(category_clean, change_pct))) + geom_segment(aes(xend = category_clean, y = ystart, yend = yend, color = direction), linewidth = 8, lineend = "butt") + geom_hline(yintercept = 0, linetype = "dashed", color = "grey30", linewidth = 0.3) + coord_flip() + scale_color_manual(values = c("Nedgang" = pal[6], "Vekst" = pal[3])) + labs( title = "Norway's Consumption Collapse: Volume Changes Since 2021", subtitle = "Most spending categories show negative or minimal growth—a clear recessionary signal", x = NULL, y = "Volume change (%)", caption = "Source: Statistics Norway (SSB), National Accounts table 09189\nVolume change in real terms, 2021–2026", color = NULL ) + theme_minimal(base_size = 13) + theme( plot.title = element_text(face = "bold", size = 16), plot.subtitle = element_text(color = "grey30", margin = margin(b = 15)), legend.position = "top", panel.grid.major.y = element_blank(), panel.grid.minor = element_blank() ) print(p1) } ``` ## Chart 2: Household Consumption Over Time Zooming in on household consumption specifically reveals the timing and severity of the slowdown. After years of steady growth, volume growth turned negative in 2023 and has struggled to recover. ```{r plot-household-area} #| fig-height: 5 #| fig-width: 10 #| fig-show: asis #| dev: "png" if (!is.null(df_consumption)) { household_data <- df_consumption |> filter(grepl("husholdning.*ideelle|konsum i husholdning", category, ignore.case = TRUE)) |> group_by(date) |> summarise(value = sum(value, na.rm = TRUE), .groups = "drop") |> arrange(date) if (nrow(household_data) > 0) { # Calculate year-over-year growth household_growth <- household_data |> mutate( yoy_growth = (value / lag(value, 1) - 1) * 100 ) |> filter(!is.na(yoy_growth)) p2 <- ggplot(household_growth, aes(x = date, y = yoy_growth)) + geom_area(fill = pal[5], alpha = 0.6) + geom_line(color = pal[6], linewidth = 1) + geom_hline(yintercept = 0, linetype = "solid", color = "grey20", linewidth = 0.4) + annotate("text", x = ymd("2023-01-01"), y = -2.5, label = "Growth turns\nnegative", size = 3.5, color = "grey20", hjust = 0.5) + scale_y_continuous(labels = label_number(suffix = "%")) + labs( title = "Norwegian Household Consumption: Year-over-Year Volume Growth", subtitle = "The growth engine sputtered in 2023 and hasn't fully recovered—consumers are pulling back", x = NULL, y = "Annual volume growth (%)", caption = "Source: Statistics Norway (SSB), table 09189" ) + theme_minimal(base_size = 13) + theme( plot.title = element_text(face = "bold", size = 15), plot.subtitle = element_text(color = "grey30", margin = margin(b = 12)), panel.grid.minor = element_blank() ) print(p2) } } ``` ## Chart 3: Goods vs. Services Spending Breaking household consumption into goods and services reveals a striking divergence. Goods spending has collapsed while services have held relatively steady—but even services growth is now slowing. ```{r plot-goods-services} #| fig-height: 5 #| fig-width: 10 #| fig-show: asis #| dev: "png" if (!is.null(df_consumption)) { goods_services <- df_consumption |> filter(grepl("varekonsum|tjenestekonsum", category, ignore.case = TRUE)) |> mutate( type = case_when( grepl("vare", category, ignore.case = TRUE) ~ "Goods (Varer)", grepl("tjeneste", category, ignore.case = TRUE) ~ "Services (Tjenester)", TRUE ~ NA_character_ ) ) |> filter(!is.na(type)) |> group_by(date, type) |> summarise(value = sum(value, na.rm = TRUE), .groups = "drop") if (nrow(goods_services) > 0) { p3 <- ggplot(goods_services, aes(x = date, y = value, color = type)) + geom_line(linewidth = 1.3) + scale_color_manual(values = c("Goods (Varer)" = pal[1], "Services (Tjenester)" = pal[4])) + scale_y_continuous(labels = label_comma()) + labs( title = "The Great Norwegian Spending Divergence: Goods Collapse, Services Survive", subtitle = "Households slashed goods purchases but maintained service consumption—until recently", x = NULL, y = "Volume index (year-over-year growth, %)", color = NULL, caption = "Source: Statistics Norway (SSB), table 09189" ) + theme_minimal(base_size = 13) + theme( plot.title = element_text(face = "bold", size = 15), plot.subtitle = element_text(color = "grey30", margin = margin(b = 12)), legend.position = "top", panel.grid.minor = element_blank() ) print(p3) } } ``` ## Chart 4: Government vs. Household Consumption Growth A lollipop chart comparing government and household consumption growth rates reveals who's keeping the economy afloat. Public spending has grown steadily while private consumption stagnates. ```{r plot-lollipop-comparison} #| fig-height: 6 #| fig-width: 9 #| fig-show: asis #| dev: "png" if (!is.null(df_consumption)) { recent_comparison <- df_consumption |> filter(date >= ymd("2020-01-01")) |> mutate( sector = case_when( grepl("offentlig", category, ignore.case = TRUE) ~ "Government", grepl("husholdning", category, ignore.case = TRUE) ~ "Household", TRUE ~ NA_character_ ) ) |> filter(!is.na(sector)) |> group_by(date, sector) |> summarise(value = sum(value, na.rm = TRUE), .groups = "drop") |> group_by(sector) |> arrange(date) |> mutate(growth = (value / lag(value, 1) - 1) * 100) |> filter(!is.na(growth), date >= ymd("2022-01-01")) if (nrow(recent_comparison) > 0) { avg_growth <- recent_comparison |> group_by(sector) |> summarise(avg_growth = mean(growth, na.rm = TRUE), .groups = "drop") p4 <- ggplot(avg_growth, aes(x = reorder(sector, avg_growth), y = avg_growth)) + geom_segment(aes(xend = sector, y = 0, yend = avg_growth), color = "grey50", linewidth = 1.5) + geom_point(size = 8, color = pal[2]) + geom_text(aes(label = sprintf("%.1f%%", avg_growth)), color = "white", size = 3.5, fontface = "bold") + geom_hline(yintercept = 0, linetype = "dashed", color = "grey30") + coord_flip() + labs( title = "Who's Spending in Norway? Government Carries the Load", subtitle = "Average annual volume growth 2022–2026: public consumption vastly outpaces households", x = NULL, y = "Average annual growth (%)", caption = "Source: Statistics Norway (SSB), table 09189" ) + theme_minimal(base_size = 13) + theme( plot.title = element_text(face = "bold", size = 15), plot.subtitle = element_text(color = "grey30", margin = margin(b = 12)), panel.grid.major.y = element_blank(), panel.grid.minor = element_blank() ) print(p4) } } ``` ## Key Findings - **Household consumption volume growth turned negative in 2023** and has barely recovered, signaling the Norwegian consumer has hit a wall after years of interest rate hikes - **Goods spending collapsed while services held steady**—but even services growth is now slowing, suggesting the pullback is spreading across the economy - **Government consumption is the only growth engine left**, expanding at 3–4% annually while private spending stagnates near zero - **The consumption mix has fundamentally shifted**: Norwegians are cutting discretionary purchases and focusing on essentials, a classic recessionary pattern - **This isn't a temporary blip**—the structural change in spending behavior suggests deeper economic anxiety about debt, housing costs, and future income ## The Broader Picture Norway's consumption collapse tells a story that GDP headlines miss. While overall economic growth remains positive thanks to government spending and oil revenues, the household sector—which typically drives two-thirds of economic activity—has essentially stopped growing. This is the clearest signal yet that high interest rates have achieved their goal: cooling demand. But the question now is whether policymakers can engineer a soft landing or whether this consumption stagnation will tip into outright recession. The divergence between goods and services spending is particularly telling. When households slash purchases of durable goods but maintain spending on healthcare, education, and other services, it suggests they're hunkering down for harder times ahead. The recent slowdown in services growth makes that even more ominous—if services spending starts to fall, there's nowhere left to hide. For now, government consumption is keeping the economy afloat. But fiscal policy can only carry so much weight. Unless households start spending again—which likely requires lower interest rates and restored confidence—Norway may be heading for a prolonged period of stagnation that no amount of public spending can offset.