Labour

Labour

Labour state is a measure of what an individual does. There are 8 distinctive categories including employment, unemployment, and retired. The encodings of these states can be found [here]](https://leeds-mrg.github.io/Minos/documentation/data_tables.html).

plot of chunk labour_barchart

plot of chunk labour_barchart

Methods

Labour state is a complex categorical data type. Single layer neural network is a simple way to estimate this state. Use multinom function from R’s nnet package. Formula for weights included given as.

\[labour\_state\_next = sex + ethnicity + age + education\_state + SF\_12 + housing\_quality + labour\_state + job\_sec + hh\_income + alcohol\_spending\]

Data

Variables used in this model. Encodings for discrete variables found in data tables.

  • sex. Biological sex male/female.

  • ethnicity. Ethnicity e.g. white british. XXXX cite.

  • age in years. XXXX cite.

  • education. Highest qualification attained. XXXX cite

  • sf12. Mental well-being score. XXXX cite

  • housing quality. Number of household appliances. Ordinal 1-3. XXXX cite

  • labour_state. Previous labour state. XXXX cite. Probably remove this. dominates prediction..

  • nssec. Socioeconomic code of job. Indicates job quality with 1-9 ordinal variable. XXXX cite

  • household income. Monthly disposable income of individuals household. XXXX cite.

  • alcohol spending. How much is spent on alcohol. XXXX cite. remove.

Results

  • hard to determine goodness of fit.

  • use confusion matrix to estimate quality of fit.

  • employed/retired well predicted. unemployed/student volatile socially and expectedly hard to predict.

  • some deterministic replacement needed for categories like student that have specific time frames. e.g. three years for a degree.

## Error in table(obs, preds): all arguments must have the same length

References