Project population development — propop • propop

Wrapper function to project population development using the cohort component method (see e.g., here and here for more details). This function calls project_raw.R, which uses matrix algebra to implement the demographic balancing equations.

You can either use your own parameters and starting population or download these data from the Swiss Federal Statistical Office (FSO). For instructions on how to download this information from STAT-TAB, see vignette("prepare_data", package = "propop").

The projection parameters need to be passed to propop::propop() as a single data frame (with the parameters as columns). The column types, names, and factor levels need to match the specifications listed below under parameters.

If nothing else is indicated in argument scenarios, propop() runs and returns all scenarios provided via parameters.

For more details on how to use this function to project the population development on the level of a canton, see vignette("project_single_region", package = "propop").

Usage

propop(
  parameters,
  population,
  year_first,
  year_last,
  scenarios = NULL,
  age_groups = 101,
  fert_first = 16,
  fert_last = 50,
  share_born_female = 100/205,
  subregional = FALSE,
  binational = TRUE,
  spatial_unit = "spatial_unit"
)

Arguments

parameters

data frame containing the FSO rates and numbers to run the projection for a specific spatial level (e.g., canton, municipality).

year, character, projection year.
spatial_unit, character, ID of spatial entity (e.g., canton, municipality) for which to run the projections.
scen, character, one or several projection scenario(s). The main scenarios are usually "reference", "low" growth, and "high" growth.
nat, character, nationality (ch = Swiss; int = foreign / international). Required if binational = TRUE.
sex, character (f = female, m = male).
age, numeric, typically ranging from 0 to 100 (incl. >100).
birthrate, numeric, number of births per mother
int_mothers, numeric, proportion of children with Swiss nationality born to non-Swiss mothers. Required if binational = TRUE.
mor, numeric, prospective mortality rate (probability of death).
acq, numeric, rate of acquisition of Swiss citizenship. Required if binational = TRUE.
emi_int, numeric, rate of people emigrating abroad (number of immigrants - number of emigrants).
emi_nat, rate of people emigrating to other cantons.
imm_int_n, numeric, number of people immigrating from abroad.
imm_nat_n, numeric, number of people immigrating from other cantons.
mig_sub (optional), numeric, net migration per subregion; this is the migration from / to other subregions (e.g., municipalities, districts) within the main superordinate projection unit (e.g., a canton). Accounts for movements between different subregions. Needs to be provided by the user.

population

data frame including the starting population of each demographic group and each spatial unit. Possible values are the same as in parameters (apart from year). The data frame only includes one year, usually the one preceding the first year of the projection.

year character, should be year_first - 1.
spatial_unit character.
nat character.
sex character.
age numeric.
n numeric, number of people per demographic group.

year_first

numeric, first year to be projected.

year_last

numeric, last year to be projected.

scenarios

(optional), character, indicating which projection scenario(s) shall be run; the corresponding information must be available in parameters. Defaults to the values in variable scen in parameters.

age_groups

numeric, number of age classes. Creates a vector with 1-year age classes running from 0 to (age_groups - 1). Must currently be set to = 101 (FSO standard number of age groups).

fert_first

numeric, first year of female fertility. Defaults to 16 (FSO standard value).

fert_last

numeric, last year of female fertility. Defaults to 50 (FSO standard value).

share_born_female

numeric, fraction of female babies. Defaults to 100 / 205 (FSO standard value).

subregional

boolean, TRUE indicates that subregional migration patterns (e.g., movement between municipalities within a canton) are part of the projection. Requires input on the level of subregions (in parameters and population).

binational

boolean, TRUE indicates that projections discriminate between two groups of nationalities. FALSE indicates that the projection is run without distinguishing between nationalities.

spatial_unit

character, name of variable containing the names of the region or subregions for which the projection shall be performed.

Value

Returns a data frame that includes the number of people for each demographic group per year (for projected years) and spatial unit. The number of rows is the product of all scenarios times all years times all demographic groups times all spatial units. The output includes several identifiers that indicate to which scenario, demographic group, year, and spatial unit the results in the rows refer to:

year: integer, indicating the projected years.
scen: character, indicating the projected scenario(s).
spatial_unit: factor, spatial units for which the projection was run (e.g., canton, districts, municipalities).
age: integer, ranging from 0n to 100 years (including those older than 100).
sex: factor, female (f) and male (m).
nat: factor, indicates if the nationality is Swiss (ch) or international / foreign (int). This variable is only returned if binational = TRUE.

The output also includes columns related to the size and change of the population:

n_jan: numric, start-of-year population per demographic group.
n_dec: numeric, end-of-year population per demographic group.
delta_n: numeric, population change per demographic group from the start to the end of the year in absolute numbers.
delta_perc: numeric, population change per demographic group from the start to the end of the year in percentages.

The components that are used to project the development of the population are also included in the output:

births: numeric, number of births (non-zero values are only available for age = 0).
mor: numeric, number of deaths.
emi_int: numeric, number of people who emigrate to other countries.
emi_nat: numeric, number of people who emigrate to other cantons.
imm_int: numeric, number of people who immigrate from other countries.
imm_nat: numeric, number of people who immigrate from other cantons.
acq: numeric, number of people who acquire Swiss citizenship (only returned if binational = TRUE.)

Examples

# Run projection for the sample data (whole canton of Aargau)
propop(
  parameters = fso_parameters,
  year_first = 2024,
  year_last = 2027,
  population = fso_population,
  subregional = FALSE,
  binational = TRUE
)
#> Running projection for: Aargau (Scenario: high)
#> ✔ Year: 2024
#> ✔ Year: 2025
#> ✔ Year: 2026
#> ✔ Year: 2027
#> Running projection for: Aargau (Scenario: low)
#> ✔ Year: 2024
#> ✔ Year: 2025
#> ✔ Year: 2026
#> ✔ Year: 2027
#> Running projection for: Aargau (Scenario: reference)
#> ✔ Year: 2024
#> ✔ Year: 2025
#> ✔ Year: 2026
#> ✔ Year: 2027
#> 
#> ── Settings used for the projection ────────────────────────────────────────────
#> Scenario(s): "high", "low", and "reference"
#> Year of starting population: 2023
#> Number of age groups: 101
#> Fertile period: 16-50
#> Share of female newborns: 0.488
#> Size of starting population: 726894
#> Projection period: 2024-2027
#> Nationality-specific projection: "yes"
#> Subregional migration: "no"
#> ────────────────────────────────────────────────────────────────────────────────
#> Projected population size by 2027:
#> - Scenario "high": 768888
#> - Scenario "low": 748703
#> - Scenario "reference": 758993
#> ════════════════════════════════════════════════════════════════════════════════
#> # A tibble: 4,848 × 17
#>     year scen  spatial_unit   age sex   nat   n_jan births   mor emi_int emi_nat
#>    <dbl> <fct> <fct>        <dbl> <fct> <fct> <dbl>  <dbl> <dbl>   <dbl>   <dbl>
#>  1  2024 high  Aargau           0 m     ch        0  2633. 9.00     6.00    21.0
#>  2  2024 high  Aargau           1 m     ch     2371     0  1.00    13.0     49.1
#>  3  2024 high  Aargau           2 m     ch     2542     0  0.998   13.0     56.0
#>  4  2024 high  Aargau           3 m     ch     2891     0  0.983   14.0     54.0
#>  5  2024 high  Aargau           4 m     ch     2766     0  0       13.0     44.0
#>  6  2024 high  Aargau           5 m     ch     2794     0  0       12.0     38.0
#>  7  2024 high  Aargau           6 m     ch     2782     0  0       11.0     33.0
#>  8  2024 high  Aargau           7 m     ch     2787     0  0       10.0     29.0
#>  9  2024 high  Aargau           8 m     ch     2726     0  0        9.00    24.0
#> 10  2024 high  Aargau           9 m     ch     2837     0  0        9.00    22.0
#> # ℹ 4,838 more rows
#> # ℹ 6 more variables: imm_int <dbl>, imm_nat <dbl>, acq <dbl>, n_dec <dbl>,
#> #   delta_n <dbl>, delta_perc <dbl>
propop(
  parameters = fso_parameters |>
    dplyr::filter(scen == "reference" | scen == "high"),
  year_first = 2024,
  year_last = 2026,
  scenarios = c("reference", "high"),
  population = fso_population,
  subregional = FALSE,
  binational = TRUE
)
#> Running projection for: Aargau (Scenario: high)
#> ✔ Year: 2024
#> ✔ Year: 2025
#> ✔ Year: 2026
#> Running projection for: Aargau (Scenario: reference)
#> ✔ Year: 2024
#> ✔ Year: 2025
#> ✔ Year: 2026
#> 
#> ── Settings used for the projection ────────────────────────────────────────────
#> Scenario(s): "high" and "reference"
#> Year of starting population: 2023
#> Number of age groups: 101
#> Fertile period: 16-50
#> Share of female newborns: 0.488
#> Size of starting population: 726894
#> Projection period: 2024-2026
#> Nationality-specific projection: "yes"
#> Subregional migration: "no"
#> ────────────────────────────────────────────────────────────────────────────────
#> Projected population size by 2026:
#> - Scenario "high": 758421
#> - Scenario "reference": 751159
#> ════════════════════════════════════════════════════════════════════════════════
#> # A tibble: 2,424 × 17
#>     year scen  spatial_unit   age sex   nat   n_jan births   mor emi_int emi_nat
#>    <dbl> <fct> <fct>        <dbl> <fct> <fct> <dbl>  <dbl> <dbl>   <dbl>   <dbl>
#>  1  2024 high  Aargau           0 m     ch        0  2633. 9.00     6.00    21.0
#>  2  2024 high  Aargau           1 m     ch     2371     0  1.00    13.0     49.1
#>  3  2024 high  Aargau           2 m     ch     2542     0  0.998   13.0     56.0
#>  4  2024 high  Aargau           3 m     ch     2891     0  0.983   14.0     54.0
#>  5  2024 high  Aargau           4 m     ch     2766     0  0       13.0     44.0
#>  6  2024 high  Aargau           5 m     ch     2794     0  0       12.0     38.0
#>  7  2024 high  Aargau           6 m     ch     2782     0  0       11.0     33.0
#>  8  2024 high  Aargau           7 m     ch     2787     0  0       10.0     29.0
#>  9  2024 high  Aargau           8 m     ch     2726     0  0        9.00    24.0
#> 10  2024 high  Aargau           9 m     ch     2837     0  0        9.00    22.0
#> # ℹ 2,414 more rows
#> # ℹ 6 more variables: imm_int <dbl>, imm_nat <dbl>, acq <dbl>, n_dec <dbl>,
#> #   delta_n <dbl>, delta_perc <dbl>