Description Usage Format Details Note Source References See Also Examples
The empirical analysis in Fruehwirth-Schnatter et al. (2011) is based on data from the Austrian Social Security Database (ASSD), which combines detailed longitudinal information on employment and earnings of all private sector workers in Austria since 1972 (see References). The IEW Working Paper Zweimueller et al. (2009) (see Source) gives an overview and a description of the main characteristics of the Austrian Social Security Database.
The ASSD was made available for the Austrian Center of Labor Economics and the Analysis of the Welfare State (http://www.labornrn.at/). The considered sample consists of N=49279 male Austrian workers, who enter the labor market for the first time in the years 1975 to 1985 and are less than 25 years old at entry. The cohort analysis is based on an observation period from 1975 to 2005.
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The format is:
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$ InitValBetas: num [1:25, 1:4] 0 0 0 0 0 0 0 0 0 0 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:25] "intercept" "unEmplRDist" "unskilled" "skilled" ...
.. ..$ : chr [1:4] "h1" "h2" "h3" "h4"
$ InitValClass: int [1:49279] 2 3 1 4 3 2 3 2 4 1 ...
$ covariates :'data.frame': 49279 obs. of 25 variables:
..$ intercept : num [1:49279] 1 1 1 1 1 1 1 1 1 1 ...
..$ unEmplRDist : num [1:49279] 0.91 0.697 0.905 0.91 1.051 ...
..$ unskilled : num [1:49279] 0 0 0 0 0 0 0 1 0 0 ...
..$ skilled : num [1:49279] 0 1 1 1 0 0 0 0 1 0 ...
..$ whiteColl : num [1:49279] 0 0 1 0 1 0 0 1 1 1 ...
..$ wageCat1Dummy: num [1:49279] 1 1 1 0 0 1 1 1 0 0 ...
..$ wageCat2Dummy: num [1:49279] 0 0 0 0 1 0 0 0 0 1 ...
..$ wageCat3Dummy: num [1:49279] 0 0 0 1 0 0 0 0 1 0 ...
..$ wageCat4Dummy: num [1:49279] 0 0 0 0 0 0 0 0 0 0 ...
..$ wageCat5Dummy: num [1:49279] 0 0 0 0 0 0 0 0 0 0 ...
..$ entryYear76 : num [1:49279] 0 0 0 0 0 0 0 0 0 0 ...
..$ entryYear77 : num [1:49279] 0 0 0 0 0 0 0 0 0 0 ...
..$ entryYear78 : num [1:49279] 0 0 0 0 0 0 0 0 0 0 ...
..$ entryYear79 : num [1:49279] 0 0 0 0 0 0 0 0 0 0 ...
..$ entryYear80 : num [1:49279] 0 0 0 0 0 0 0 0 0 0 ...
..$ entryYear81 : num [1:49279] 0 0 0 0 0 0 0 0 0 0 ...
..$ entryYear82 : num [1:49279] 0 0 0 0 0 0 0 0 0 0 ...
..$ entryYear83 : num [1:49279] 0 0 0 0 0 0 0 0 0 0 ...
..$ entryYear84 : num [1:49279] 0 0 0 0 0 0 0 0 0 0 ...
..$ entryYear85 : num [1:49279] 0 0 0 0 0 0 0 0 0 0 ...
..$ ia.ueRD.wc1D : num [1:49279] 0.91 0.697 0.905 0 0 ...
..$ ia.ueRD.wc2D : num [1:49279] 0 0 0 0 1.05 ...
..$ ia.ueRD.wc3D : num [1:49279] 0 0 0 0.91 0 ...
..$ ia.ueRD.wc4D : num [1:49279] 0 0 0 0 0 0 0 0 0 0 ...
..$ ia.ueRD.wc5D : num [1:49279] 0 0 0 0 0 0 0 0 0 0 ...
$ mccXiPrior :List of 1
..$ :List of 1
.. ..$ xi: num [1:6, 1:6] 0.7 0.15 0.0333 0.0333 0.0333 ...
$ NjkiMat : num [1:49279, 1:36] 0 0 0 2 7 0 4 0 0 1 ...
$ Njk.i : num [1:6, 1:6, 1:49279] 0 0 0 0 0 0 0 1 1 0 ...
..- attr(*, "dimnames")=List of 3
.. ..$ : chr [1:6] "0" "1" "2" "3" ...
.. ..$ : chr [1:6] "0" "1" "2" "3" ...
.. ..$ : NULL
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LMEntryPaperData
is a list containing the following objects:
InitValBetas
contains a matrix with the initial values (used in our paper) for the logit regression coefficients.
InitValClass
contains a vector with some initial values (used in our paper) for the classification variable (group membership for 4 groups).
covariates
contains the data.frame with the covariates used in the logit regression model. It contains the following variables:
unEmplRDist | unemployment rate in the district | ||
unskilled | dummy for unskilled workers | ||
skilled | dummy for skilled workers | ||
whiteColl | dummy for white collar workers | ||
wageCat1Dummy,..., | |||
wageCat5Dummy | dummies for starting in the corresponding wage category | ||
entryYear76,..., | |||
entryYear85 | dummies for starting in the corresponding year | ||
ia.ueRD.wc1D,..., | |||
ia.ueRD.wc5D | interaction variable for unemployment rate in the | ||
district and the dummies for starting in the | |||
corresponding wage category |
mccXiPrior
contains the prior-parameters (used in the paper) for the transition matrices.
NjkiMat
contains the Njk.i
-data in matrix format of dimension 49279x36
(each row corresponds to the columns of the matrices in Njk.i
).
Njk.i
contains the transition frequencies in a 3-dim array of dimension 6x6x49279 containing the transition frequencies (6 x 6-matrices) of 49279 individuals. These represent the counts of transitions between wage categories from year to year with varying observation periods. Categories 1 to 5 correspond to the wage quintiles and 0 to no income.
Note, that in contrast to the literature (see References), the numbering (labelling) of the states of the categorical outcome variable (time series) in this package is sometimes 0,...,K (instead of 1,...,K), however, there are K+1 categories (states)!
The following IEW Working Paper gives an overview and a description of the main characteristics of the Austrian Social Security Database:
Zweimueller, Josef, Winter-Ebmer, Rudolf, Lalive, Rafael, Kuhn, Andreas, Wuellrich, Jean-Philippe, Ruf, Oliver and Buechi, Simon, Austrian Social Security Database (May 4, 2009). Available at SSRN: http://ssrn.com/abstract=1399350 or at http://www.labornrn.at/wp/wp0903.pdf.
Sylvia Fruehwirth-Schnatter, Christoph Pamminger, Andrea Weber and Rudolf Winter-Ebmer, (2011), "Labor market entry and earnings dynamics: Bayesian inference using mixtures-of-experts Markov chain clustering". Journal of Applied Econometrics. DOI: 10.1002/jae.1249 http://onlinelibrary.wiley.com/doi/10.1002/jae.1249/abstract
Link to Journal of Applied Econometrics Data Archive: http://econ.queensu.ca/jae/forthcoming/fruehwirth-schnatter-et-al/
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data(LMEntryPaperData)
str(LMEntryPaperData)
# ===================== LMEntry Paper Data =================================
#rm(list=ls(all=TRUE))
# set working directory
curDir <- getwd()
if ( !file.exists("bayesMCClust-wd") ) dir.create("bayesMCClust-wd")
setwd("bayesMCClust-wd")
myOutfilesDir <- "LMEntry-Paper-Data-Outfiles"
# ==============================================================================
if (!is.element("LMEntryPaperData$covariates", search())) {
attach(LMEntryPaperData$covariates)
}
# ==============================================================================
groupNr <- 4
# ==============================================================================
if ( FALSE ) {
try(mcClustExtended( # parameter lists (all four) must be complete!!!
Data=list(dataFile=LMEntryPaperData$Njk.i,
storeDir=myOutfilesDir,
priorFile= LMEntryPaperData$mccXiPrior,
X = cbind( intercept=1, unEmplRDist, unskilled, skilled, whiteColl,
wageCat1Dummy, wageCat2Dummy, wageCat3Dummy,
wageCat4Dummy, wageCat5Dummy,
entryYear76, entryYear77, entryYear78,
entryYear79, entryYear80, entryYear81,
entryYear82, entryYear83, entryYear84,
entryYear85,
ia.ueRD.wc1D, ia.ueRD.wc2D, ia.ueRD.wc3D,
ia.ueRD.wc4D, ia.ueRD.wc5D
) ),
Prior=list(H=groupNr,
c=1,
cOff=1,
usePriorFile=TRUE,
xiPooled=TRUE,
N0=10,
betaPrior = "informative", # N(0,1)
betaPriorMean = 0,
betaPriorVar = 1),
Initial=list(xi.start.ind=3,
pers=0.7,
S.i.start = LMEntryPaperData$InitValClass,
Beta.start = LMEntryPaperData$InitValBetas ),
Mcmc=list(M=15000,
M0=10000,
mOut=500,
mSave=5000,
seed=3546541)
))
}
setwd(curDir)
if (is.element("LMEntryPaperData$covariates", search())) {
detach(LMEntryPaperData$covariates)
}
# ==============================================================================
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