Geographical area and type of municipalities [34_215_DF_DCCV_ARRETRATI_6]

Updated on DBnomics on July 21, 2023 (8:11 AM)

Frequency [FREQ]
Territory [REF_AREA]
Indicator [DATA_TYPE]
Measure [MEASURE]
Expenditures [COICOP_LIST]
Household number of components [NUMBER_HOUSEHOLD_COMP]
Household typology [HOUSEHOLD_TYPOLOGY]
Number of children [NUMB_OF_CHILDREN]
Number of elderly [NUMB_OF_ELDERLY]
Sex of main income earner [SEX_MAIN_PERCEPTOR]
Age of main income earner [AGE_MAIN_EARNIER]
Educational level of main income earner [EDU_LEV_MAIN_EARN]
Professional status of main income earner [LABPROF_STATUS_C_MAIN_EARNER]

Dataset has 45 series. Add search filters to narrow them.

Dimension codes and labels
[FREQ] Frequency
  • [A] annual
[REF_AREA] Territory
  • [1] metropolitan area
  • [2] metropolitan area - centre
  • [3] metropolitan area - suburbs
  • [4] big municipality
  • [5] small municipality
  • [6] until 2,000 inhab.
  • [7] 2,001 - 10,000 inhab.
  • [8] 10,001 - 50,000 inhab.
  • [9] 50,001 inhab. and over
  • [IT] Italy
  • [ITC] Nord-ovest
  • [ITD] Nord-est
  • [ITE] Centro (I)
  • [ITF] Sud
  • [ITG] Isole
[DATA_TYPE] Indicator
  • [FAM_ARR_SPESA] households who have arrears on selected items (percent of households that bought that item)
[MEASURE] Measure
  • [10] per hundred values
[COICOP_LIST] Expenditures
  • [1] utility bills
  • [2] rent or mortgage (for the dwelling)
  • [3] debt repayments (exc. dwelling)
[NUMBER_HOUSEHOLD_COMP] Household number of components
  • [99] total
[HOUSEHOLD_TYPOLOGY] Household typology
  • [99] total
[NUMB_OF_CHILDREN] Number of children
  • [9] total
[NUMB_OF_ELDERLY] Number of elderly
  • [9] total
[SEX_MAIN_PERCEPTOR] Sex of main income earner
  • [9] total
[AGE_MAIN_EARNIER] Age of main income earner
  • [9] total
[EDU_LEV_MAIN_EARN] Educational level of main income earner
  • [99] total
[LABPROF_STATUS_C_MAIN_EARNER] Professional status of main income earner
  • [99] total
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