ffs_demo {migest}

Estimation of bilateral migrant flows from bilateral migrant stocks using demographic accounting approaches

Description

Estimates migrant transitions flows between two sequential migrant stock tables. Replaces old ffs.

Usage

ffs_demo(
  stock_start = NULL,
  stock_end = NULL,
  births = NULL,
  deaths = NULL,
  seed = NULL,
  stayer_assumption = TRUE,
  match_global = "before-demo-adjust",
  match_birthplace_tot_method = "rescale",
  birth_method = "native",
  birth_non_negative = TRUE,
  death_method = "proportion",
  verbose = FALSE,
  return = "flow"
)

Arguments

stock_start

Matrix of migrant stock totals at time t. Rows in the matrix correspond to place of birth and columns to place of residence at time t. Previously had argument name m1.

stock_end

Matrix of migrant stock totals at time t+1. Rows in the matrix correspond to place of birth and columns to place of residence at time t+1. Previously had argument name m2.

births

Vector of the number of births between time t and t+1 in each region. Previously had argument name b_por.

deaths

Vector of the number of deaths between time t and t+1 in each region. Previously had argument name d_por.

seed

Matrix of auxiliary data. By default set to 1 for all origin-destination combinations. Previously had argument name m.

stayer_assumption

Logical value to indicate whether to use a quasi-independent or independent IPFP to estimate flows. By default uses quasi-independent, i.e. is set to TRUE and estimates the minimum migration. When set to FALSE estimates flows under the independent model as used as part of Azose and Raftery (2019).

match_global

Character string used to indicate whether to balance the change in stocks totals with the changes in births and deaths. Only applied when match_birthplace_tot_method is either rescale or rescale-adjust-zero-fb. By default uses after-demo-adjust rather than before-demo-adjust which I think minimises risk of negative values.

match_birthplace_tot_method

Character string passed to method argument in match_birthplace_tot to ensure place of birth margins in stock tables match.

birth_method

Character string passed to method argument in birth_mat.

birth_non_negative

Logical value passed to non_negative argument in birth_mat.

death_method

Character string passed to method argument in death_mat.

verbose

Logical value to show progress of the estimation procedure. By default FALSE.

return

Character string used to indicate whether to return the array of estimated flows when set to flow (default), array of demographic accounts when set to account or the demographic account, list of input settings and the origin-destination matrix when set to classic

Value

Estimates migrant transitions flows between two sequential migrant stock tables using various methods. See the example section for possible variations on estimation methods.

Detail of returned object varies depending on the setting used in the return argument.

Author(s)

Guy J. Abel

References

Abel and Cohen (2019) Bilateral international migration flow estimates for 200 countries Scientific Data 6 (1), 1-13

Azose & Raftery (2019) Estimation of emigration, return migration, and transit migration between all pairs of countries Proceedings of the National Academy of Sciences 116 (1) 116-122

Abel, G. J. (2018). Estimates of Global Bilateral Migration Flows by Gender between 1960 and 2015. International Migration Review 52 (3), 809–852.

Abel, G. J. and Sander, N. (2014). Quantifying Global International Migration Flows. Science, 343 (6178) 1520-1522

Abel, G. J. (2013). Estimating Global Migration Flow Tables Using Place of Birth. Demographic Research 28, (18) 505-546

See Also

ffs_diff, ffs_rates

Examples

##
## without births and deaths over period
##
# data as in demographic research and science paper papers
s1 <- matrix(data = c(1000, 100, 10, 0, 55, 555, 50, 5, 80, 40, 800, 40, 20, 25, 20, 200),
             nrow = 4, ncol = 4, byrow = TRUE)
s2 <- matrix(data = c(950, 100, 60, 0, 80, 505, 75, 5, 90, 30, 800, 40, 40, 45, 0, 180),
             nrow = 4, ncol = 4, byrow = TRUE)
b <- d <- rep(0, 4)
r <- LETTERS[1:4]
dimnames(s1) <- dimnames(s2) <- list(birth =  r, dest = r)
names(b) <- names(d) <- r
addmargins(s1)
addmargins(s2)
b
d

# demographic research and science paper example
e0 <- ffs_demo(stock_start = s1, stock_end = s2, births = b, deaths = d)
e0
sum_od(e0)

# international migration review paper example
s1[,] <- c(100, 20, 10, 20, 10, 55, 40, 25, 10, 25, 140, 20, 0, 10, 65, 200)
s2[,] <- c(70, 25, 10, 40, 30, 60, 55, 45, 10, 10, 140, 0, 10, 15, 50, 180)
addmargins(s1)
addmargins(s2)

e1 <- ffs_demo(stock_start = s1, stock_end = s2, births = b, deaths = d)
sum_od(e1)

# international migration review supp. material example
# distance matrix
dd <- matrix(data = c(0, 5, 50, 500, 5, 0, 45, 495, 50, 45, 0, 450, 500, 495, 450, 0),
             nrow = 4, ncol = 4, byrow = TRUE)
dimnames(dd) <- list(orig = r, dest = r)
dd
e2 <- ffs_demo(stock_start = s1, stock_end = s2, births = b, deaths = d, seed = dd)
sum_od(e2)

##
## with births and deaths over period
##
# demographic research paper example (with births and deaths)
s1[,] <- c(1000, 55, 80, 20, 100, 555, 40, 25, 10, 50, 800, 20, 0, 5, 40, 200)
s2[,] <- c(1060, 45, 70, 30, 60, 540, 75, 30, 10, 40, 770, 20, 10, 0, 70, 230)
b[] <- c(80, 20, 40, 60)
d[] <- c(70, 30, 50, 10)
e3 <- ffs_demo(stock_start = s1, stock_end = s2, 
               births = b, deaths = d, 
               match_birthplace_tot_method = "open-dr")
sum_od(e3)
# makes more sense to use this method
e4 <- ffs_demo(stock_start = s1, stock_end = s2, 
               births = b, deaths = d, 
               match_birthplace_tot_method = "open")
sum_od(e4)

# science paper  supp. material example
b[] <- c(80, 20, 60, 60)
e5 <- ffs_demo(stock_start = s1, stock_end = s2, births = b, deaths = d)
sum_od(e5)

# international migration review supp. material example (with births and deaths)
s1[,] <- c(100, 20, 10, 20, 10, 55, 40, 25, 10, 25, 140, 20, 0, 10, 65, 200)
s2[,] <- c(75, 20, 30, 30, 25, 45, 40, 30, 5, 30, 150, 20, 0, 15, 60, 230)
b[] <- c(10, 50, 25, 60)
d[] <- c(30, 10, 40, 10)
e6 <- ffs_demo(stock_start = s1, stock_end = s2, births = b, deaths = d)
sum_od(e6)

# scientific data 2019 paper
s1[] <- c(100, 80, 30, 60, 10, 180, 10, 70, 10, 10, 140, 10, 0, 90, 40, 160)
s2[] <- c(95, 75, 55, 35, 5, 225, 0, 25, 15, 5, 115, 25, 5, 55, 50, 215)
b[] <- c(0, 0, 0, 0)
d[] <- c(0, 0, 0, 0)
e7 <- ffs_demo(stock_start = s1, stock_end = s2, births = b, deaths = d)
sum_od(e7)