Calculate Career Complexity Metrics for General Career Analysis

Calculates career complexity metrics for generalized career trajectory analysis, extending beyond pre/post event evaluation. Analyzes concurrent employment patterns, employment diversity measures, and complexity indices across any time period.

Usage

calculate_career_complexity_metrics(
  data,
  id_column = "cf",
  time_period_column = NULL,
  complexity_variables = c("over_id", "arco", "prior")
)

Arguments

data

A data.table containing employment records

id_column

Character. Name of person identifier column. Default: "cf"

time_period_column

Character. Optional column for grouping by time periods. If NULL, analyzes entire career trajectory. Default: NULL

complexity_variables

Character vector. Variables to use for complexity calculation. Default: c("over_id", "arco", "prior")

Value

A data.table with career complexity metrics:

cf

Person identifier

time_period

Time period (if time_period_column provided)

max_concurrent_jobs

Maximum number of concurrent jobs

avg_concurrent_jobs

Average number of concurrent jobs

concurrent_employment_days

Days with multiple concurrent jobs

concurrent_employment_rate

Proportion of employment with multiple jobs

employment_diversity_index

Shannon diversity index of employment types

career_complexity_index

Overall job complexity score

career_fragmentation_index

Measure of career fragmentation

Details

The complexity score has been enhanced for better discriminatory power, using an improved formula that provides greater variability across different career patterns.

Examples

if (FALSE) { # \dontrun{
# Analyze overall career complexity
career_complexity <- calculate_career_complexity_metrics(
  data = employment_data,
  complexity_variables = c("over_id", "arco", "sector", "contract_type")
)

# Analyze complexity by year
yearly_complexity <- calculate_career_complexity_metrics(
  data = employment_data,
  time_period_column = "year"
)
} # }