LaborParticipation | R Documentation |

The labor participation (yes/no) of 1583 women was measured in five consecutive year, 1967-1971,
leading to a `2\times 2\times 2\times 2\times 2`

table.

The data are tabulated in Bergsma, Croon, and Hagenaars (2009, p. 128).

Section 4.3 in Bergsma, Croon and Hagenaars, 2009

`data(LaborParticipation)`

A data frame with 1583 observations on the following variables.

`Year1967`

Participated in 1967 (factor): 1 = No; 2 = Yes.

`Year1968`

Participated in 1968 (factor): 1 = No; 2 = Yes.

`Year1969`

Participated in 1969 (factor): 1 = No; 2 = Yes.

`Year1970`

Participated in 1970 (factor): 1 = No; 2 = Yes.

`Year1971`

Participated in 1971 (factor): 1 = No; 2 = Yes.

Heckman & Willis (1977).

Bergsma, W. P., Croon, M. A., & Hagenaars, J. A. P. (2009).
*Marginal models for dependent, clustered, and longitudinal categorical data.*
New York: Springer.

Heckman, J. J. & Willis, R. J. (1977).
A beta-logistic model for the analysis of sequential labor force participation by married women.
*Journal of Political Economy, 85*, 27-58.

```
data(LaborParticipation)
n <- c(t(ftable(LaborParticipation)))
##########################################################
#Sample kappa values
#matrix for obtaining transition matrices for consecutive years
at <- MarginalMatrix(var = c("67", "68", "69", "70", "71"),
marg = list(c("67", "68") ,c("68", "69"), c("69", "70"), c("70", "71")),
dim = c(2, 2, 2, 2, 2))
coeff <- SpecifyCoefficient("CohenKappa", arg = 2, rep = 4);
stats <- SampleStatistics(n, list(coeff,at), ShowParameters = FALSE)
##########################################################
#Fitting models for kappa
#matrix for obtaining transition matrices for consecutive years
at <- MarginalMatrix(var = c("67", "68", "69", "70", "71"),
marg = list(c("67", "68") ,c("68", "69"), c("69", "70"), c("70", "71")),
dim = c(2, 2, 2, 2, 2))
coeff <- SpecifyCoefficient("CohenKappa", arg = 2, rep = 4);
bt1 <- ConstraintMatrix(var = c(1), suffconfigs = c(), dim = c(4)); #equal kappas
bt2 <- rbind(c(1,-2,1,0), c(0,1,-2,1)); #linear trend for kappas
model <- list(bt1, coeff,at)
m = MarginalModelFit(n, model, ShowParameters = FALSE, ShowProgress = 10)
```

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