Overview

This vignette explains how to use propop::propop() to perform population projections for a single region such as a canton (for projections of subregions, see this vignette).

The function was tailored to the context of Switzerland. It uses the cohort component method for cantonal scenarios, which was developed by the Federal Statistical Office (FSO, 2020; only available in French). To run the function, you need the following input:

• a data frame with the starting population, that is, the most up-to-date number of people for each demographic group before the first projection year; to illustrate, the example population data in propop are from 31. December 2018 and the first projection year is 2019.

• a data frame containing model parameters, that is, information about how key demographic variables such as mortality are expected to develop in the future;

• some global arguments which do not change over time or across demographic groups (e.g., proportion of female to male newborns).

Importantly, the two data frames’ structure (number, names, type of columns) must correspond exactly to the specifications shown in this vignette. Among other things, it is mandatory to provide two levels for sex. Nationality can have either one or two levels. The function requires 1-year age groups ranging from 0 to 100 (incl. those who are older).

Projections for a single region

The package propop includes the population data from the canton of Aargau from 2018 and the FSO parameters from the population development scenarios 2020. Using these resources, we can project the population for the canton as a whole for 1-year age groups for the period 2019-2030.

The start and end of women’s fertile period, the proportion of babies born as female, and the share of babies born by mothers who are not Swiss are stable parameters that are defined in propop::project_raw() and passed to propop::propop().

projection_canton_2030 <- propop(
  parameters = fso_parameters,
  year_first = 2019,
  year_last = 2030,
  population = fso_population,
  subregional = FALSE,
  binational = TRUE
)
#> Running projection for: Aargau
#> ✔ Year: 2019
#> ✔ Year: 2020
#> ✔ Year: 2021
#> ✔ Year: 2022
#> ✔ Year: 2023
#> ✔ Year: 2024
#> ✔ Year: 2025
#> ✔ Year: 2026
#> ✔ Year: 2027
#> ✔ Year: 2028
#> ✔ Year: 2029
#> ✔ Year: 2030
#> 
#> ── Settings used for the projection ────────────────────────────────────────────
#> Year of starting population: 2018
#> Number of age groups: 101
#> Fertile period: 16-50
#> Share of female newborns: 0.488
#> Size of starting population: 678207
#> Projection period: 2019-2030
#> Projected population size (2030): 773105
#> Nationality-specific projection: "yes"
#> Subregional migration: "no"
#> ────────────────────────────────────────────────────────────────────────────────

projection_canton_2030 |>
  # round to 2 digits
  dplyr::mutate(across(n_jan:n_dec, \(x) sprintf(fmt = "%.0f", x))) |>
  DT::datatable(filter = "top") |> 
  DT::formatStyle(
  'n_jan',
  backgroundColor = '#ffcc8f'
) |> 
  DT::formatStyle(
  c("births", "mor", "emi_int", "emi_nat", "imm_int", "imm_nat", "acq"),
  backgroundColor = '#96D4FF'
) |> 
  DT::formatStyle(
  'n_dec',
  backgroundColor = '#007AB8'
)  

The output file includes the number of people in January (n_jan) and the number of people in December (n_dec) per demographic group and year. The components that are used to project the population growth from the start to the end of the year are also included in the output. Thus, all elements of the cohort component equation are directly available from the output. Note that the number of people who acquire the Swiss citizenship (acq) are added if the demographic group is Swiss and subtracted if the demographic group has a different nationality.

No distinction between nationalities

As of version 0.2.0 it is possible to run projections without distinguishing between Swiss and non-Swiss nationals. The simplest way to achieve this is to provide population data and parameters without the nationality-specific columns (remove nat, acq, births_int_ch).

Let’s adapt the input files accordingly. To keep things simple, we run the projection for only one of the two nationalities. It goes without saying that using propop::propop() like this in real settings requires more preparation (e.g., determining a single value for the joint population when parameters differ between Swiss and non-Swiss people).

fso_parameters_int <- fso_parameters |>
  # drop Swiss people
    dplyr::filter(nat == "int") |>
  #   remove `nat`, `acq` and `births_int_ch` from `parameters`
    dplyr::select(-c(nat, acq, int_mothers))

fso_population_int <- fso_population |> 
# drop Swiss people
    dplyr::filter(nat == "int") |>
    # remove `nat` from `population`
     dplyr::select(-nat)

When calling propop::propop(), you need to set binational = FALSE.

projection_int <- propop(
  parameters = fso_parameters_int,
  year_first = 2019,
  year_last = 2030,
  population = fso_population_int,
  subregional = FALSE,
  binational = FALSE
)
#> Running projection for: Aargau
#> ✔ Year: 2019
#> ✔ Year: 2020
#> ✔ Year: 2021
#> ✔ Year: 2022
#> ✔ Year: 2023
#> ✔ Year: 2024
#> ✔ Year: 2025
#> ✔ Year: 2026
#> ✔ Year: 2027
#> ✔ Year: 2028
#> ✔ Year: 2029
#> ✔ Year: 2030
#> 
#> ── Settings used for the projection ────────────────────────────────────────────
#> Year of starting population: 2018
#> Number of age groups: 101
#> Fertile period: 16-50
#> Share of female newborns: 0.488
#> Size of starting population: 170424
#> Projection period: 2019-2030
#> Projected population size (2030): 253826
#> Nationality-specific projection: "no"
#> Subregional migration: "no"
#> ────────────────────────────────────────────────────────────────────────────────

projection_int |>
  # round to two digits
  dplyr::mutate(across(n_jan:n_dec, \(x) sprintf(fmt = "%.0f", x))) |>
  DT::datatable(filter = "top")