mvad {MEDseq}

MVAD: Transition from school to work

Description

The data comes from a study by McVicar and Anyadike-Danes on transition from school to work. The data consist of static background characteristics and a time series sequence of 72 monthly labour market activities for each of a cohort of 712 individuals in the Status Zero Survey. The individuals were followed up from July 1993 to June 1999. The monthly states are recorded in columns 15 (Jul.93) to 86 (Jun.99).

Usage

data(mvad)

Format

A data frame containing 712 rows, 72 state variables, 1 id variable and 13 covariates.

Details

States are:

employment	(EM)
FE	further education (FE)
HE	higher education (HE)
joblessness	(JL)
school	(SC)
training	(TR)

The data set contains also ids (id) and sample weights (weights) as well as the following binary covariates:

male
catholic
Belfast, N.Eastern, Southern, S.Eastern, Western (location of school, one of five Education and Library Board areas in Northern Ireland)
Grammar (type of secondary education, 1=grammar school)
funemp (father's employment status at time of survey, 1=father unemployed)
gcse5eq (qualifications gained by the end of compulsory education, 1=5+ GCSEs at grades A-C, or equivalent)
fmpr (SOC code of father's current or most recent job at time of survey, 1=SOC1 (professional, managerial or related))
livboth (living arrangements at time of first sweep of survey (June 1995), 1=living with both parents)

Note

The first two months of the observation period coincide with summer holidays from school. Hence, documented examples throughout this package extract only the states in columns 17 to 86; i.e. sequences of length 70 from Sep.93 to Jun.99.

Source

McVicar and Anyadike-Danes (2002)

References

McVicar, D. (2000). Status 0 four years on: young people and social exclusion in Northern Ireland. Labour Market Bulletin, 14, 114-119.

McVicar, D. and Anyadike-Danes, M. (2002). Predicting successful and unsuccessful transitions from school to work by using sequence methods. Journal of the Royal Statistical Society: Series A (Statistics in Society), 165(2): 317-334.

Examples

data(mvad, package="MEDseq")

mvad$Location <- factor(apply(mvad[,5:9], 1L, function(x) 
                 which(x == "yes")), labels = colnames(mvad[,5:9]))
mvad          <- list(covariates = mvad[c(3:4,10:14,87)],
                      sequences = mvad[,15:86], 
                      weights = mvad[,2])
mvad.cov      <- mvad$covariates

# Create a state sequence object with the first two (summer) time points removed
states        <- c("EM", "FE", "HE", "JL", "SC", "TR")
labels        <- c("Employment", "Further Education", "Higher Education", 
                   "Joblessness", "School", "Training")
mvad.seq      <- seqdef(mvad$sequences[-c(1,2)], states=states, 
                        labels=labels, weights=mvad$weights)