US workers compensation datasets

The dataset usworkcomp is originally from the National Council on Compensation Insurance and was examined by Klugman (1992), Frees et al. (2001) and Frees (2011). This database contains records of losses due to permanent or partial disability claims for workers compensation insurance in US. For each claim amount, the payroll is available as a measure of exposure units. A total of 847 data points is available coming from the observation of 121 risk classes over 7 years.

The dataset usworkcomptri8807 comes from an unknown US insurer: this reserve triangle was used in Lacoume (2007).

Usage

data(usworkcomp)

Format

usworkcomp is a data frame of 4 columns and 847 rows:

CL

Occupation class identifier, 1-124.

YR

Year identifier, 1-7.

PR

Payroll, a measure of exposure to loss, in dollars.

LOSS

Losses related to permanent partial disability, in dollars.

usworkcomptri8807 is a reserve triangle with 21 development years and 20 accident years.

Source

FreesBook-RMAFA

References

Klugman, S.A. (1992). Bayesian Statistics in Actuarial Science, Kluwer, Boston, doi:10.1007/978-94-017-0845-6 .

Frees, E.W. and Young, V.R. and Luo, Y. (2001), Case studies using panel data models, North American Actuarial Journal, 5, 24-42, doi:10.1080/10920277.2001.10596010 .

Lacoume, A. (2007), Mesure du risque de reserve sur un horizon de un an, Actuary memoir, ISFA.

Frees, E.W. (2011). Regression Modeling with Actuarial and Financial Applications, Cambridge University Press, doi:10.1017/CBO9780511814372 .

Examples

# (1) load of data
#
data(usworkcomp)


# Table 3 of Fres et al. (2001)
# (in million USD)

t(sapply(unique(usworkcomp$YR), 
  function(y) summary( subset(usworkcomp, YR == y)[,"PR"] / 10^6 )))
#>          Min.   1st Qu.   Median     Mean   3rd Qu.     Max.
#> [1,] 0.000000  8.678921 24.67941 143.1963  76.86118 3416.862
#> [2,] 0.011130  9.803526 27.00537 156.8266  83.11849 3784.782
#> [3,] 0.276172  8.784862 31.30073 172.3303  96.58976 4284.556
#> [4,] 0.116042 10.595191 33.94508 187.7895 101.53356 4866.862
#> [5,] 0.013727 10.068619 35.09303 201.9447 110.77980 5559.892
#> [6,] 0.000000  8.299929 34.72810 198.0189 110.01662 5948.228
#> [7,] 0.007509  8.337246 29.80313 192.7985 116.53655 6137.275