LMEntryPaperData: Data From Fruehwirth-Schnatter et al. (2011): "Labor market...

Description Usage Format Details Note Source References See Also Examples

Description

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.

Usage

1

Format

The format is:

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List of 6
 $ 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
 

Details

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

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)!

Source

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.

References

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/

See Also

mcClustExtended

Examples

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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)
}
# ==============================================================================

bayesMCClust documentation built on May 29, 2017, 3:31 p.m.