migest (version 1.8.5)

ffs_demo: 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(
  m1 = NULL,
  m2 = NULL,
  b_por = NULL,
  d_por = NULL,
  m = NULL,
  stayer_assumption = TRUE,
  match_global = "before-demo-adjust",
  match_pob_tot_method = "rescale",
  birth_non_negative = TRUE,
  death_method = "proportion",
  verbose = FALSE,
  ...
)

Arguments

m1

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

m2

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.

b_por

Vector of the number of births between time t and t+1 in each region.

d_por

Vector of the number of deaths between time t and t+1 in each region.

m

Matrix of auxiliary data. By default set to 1 for all origin-destination combinations.

stayer_assumption

Logical value to indicate whether to use ipf3 or ipf3_qi to estimate flows. By default uses ipf3_qi, i.e. is set to TRUE. The ipf function is useful for replicating method of Azoze and Raferty.

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_pob_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_pob_tot_method

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

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 indicate the print the parameter estimates at each iteration of the various IPF routines. By default FALSE.

...

Additional arguments passes to ipf3_qi or ipf3.

Value

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

Returns a list object with:

mu

Array of indirect estimates of origin-destination matrices by place of birth.

it

Iteration count.

tol

Tolerance level at final iteration.

y

Array of indirect estimates of origin-destination matrices by place of birth with additional rows and columns for births, deaths and moves to other regions.

...

Slots to record which estimation method was used (as set by arguments above)

od_flow

Matrix of estimated origin-destination flows

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<U+2013>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

ipf3_qi, ffs_diff, ffs_rates

Examples

# NOT RUN {
##
## without births and deaths over period
##
# data as in 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)
reg <- LETTERS[1:4]
dimnames(s1) <- dimnames(s2) <- list(pob = reg, por = reg)
names(b) <- names(d) <- reg
s1; s2; b; d

# demographic research and science paper example
ffs_demo(m1 = s1, m2 = s2, b_por = b, d_por = d)

# 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)
ffs_demo(m1 = s1, m2 = s2, b_por = b, d_por = d)

# international migration review supp. material example
dm <- 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(dm) <- list(orig = reg, dest = reg)
ffs_demo(m1 = s1, m2 = s2, b_por = b, d_por = d, m = dm)

##
## with births and deaths over period
##
# demographic research paper example
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)
ffs_demo(m1 = s1, m2 = s2, b_por = b, d_por = d, match_pob_tot_method = "open-dr")
# makes more sense to use this method
ffs_demo(m1 = s1, m2 = s2, b_por = b, d_por = d, match_pob_tot_method = "open")

# science paper  supp. material example
b[] <- c(80, 20, 60, 60)
ffs_demo(m1 = s1, m2 = s2, b_por = b, d_por = d)

# international migration review supp. material example
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)
ffs_demo(m1 = s1, m2 = s2, b_por = b, d_por = d)
# }