Analyze Consolidated Employment Periods Using over_id

Provides comprehensive analysis of consolidated employment periods leveraging the over_id functionality from vecshift() output. This function analyzes employment periods grouped by over_id to understand the benefits and patterns of employment consolidation, comparing individual contracts to consolidated employment episodes.

Usage

analyze_consolidated_periods(
  segments,
  level = "both",
  include_unemployment = TRUE,
  consolidation_type = "both",
  min_employment_duration = 1,
  include_details = TRUE
)

Arguments

segments

Input data from vecshift() with over_id column. Must be a data.table with required columns: cf, inizio, fine, arco, durata, over_id, and optionally additional employment attributes for detailed analysis.

level

Character string specifying analysis granularity:

• "person": Person-level analysis with individual statistics

• "aggregate": Population-level aggregate statistics

• "both": Both person and aggregate analysis (default)

include_unemployment

Logical. If TRUE (default), includes unemployment periods (over_id = 0) in the analysis. If FALSE, focuses only on employment consolidation.

consolidation_type

Character string specifying consolidation approach for comparison:

• "both": Compare against both overlapping and consecutive consolidation (default)

• "overlapping": Compare against overlapping consolidation only

• "employment_only": Focus on employment periods only, ignore unemployment

min_employment_duration

Minimum duration (days) for employment periods to include in analysis (default: 1). Helps filter out very short-term contracts.

include_details

Logical. If TRUE (default), includes detailed breakdowns and comparisons. If FALSE, returns only summary statistics for performance.

Value

When level is "person" or "both", returns a list containing:

• person_analysis: data.table with person-level statistics including:

  • cf: Person identifier

  • total_periods_original: Count of original vecshift segments

  • total_periods_consolidated: Count of consolidated periods (unique over_id)

  • employment_periods_original: Employment segments (arco >= 1)

  • employment_periods_consolidated: Consolidated employment periods (over_id > 0)

  • unemployment_periods: Unemployment segments (over_id = 0)

  • consolidation_ratio: Reduction in period count (1 - consolidated/original)

  • employment_complexity_avg: Average arco for employment periods

  • max_simultaneous_jobs: Maximum concurrent employment (max arco)

  • total_employment_days_original: Sum of original employment durations

  • total_employment_days_consolidated: Sum of consolidated employment durations

  • avg_employment_duration_original: Mean duration of original employment segments

  • avg_employment_duration_consolidated: Mean duration of consolidated periods

  • duration_efficiency: Duration consistency (consolidated/original ratio)

  • overlapping_episodes: Count of over_id periods with multiple contracts

• aggregate_analysis: data.table with population-level summary statistics

• consolidation_benefits: data.table showing consolidation impact analysis

• employment_complexity: data.table with employment complexity patterns

When level is "aggregate", returns only the aggregate analysis components.

All results include attributes:

• analysis_parameters: Analysis configuration used

• validation_results: Data validation and quality checks

• computation_time: Time taken for analysis

Details

The over_id column from vecshift() identifies continuous overlapping employment periods:

• over_id = 0: Unemployment periods (no active contracts)

• over_id > 0: Employment periods with same value for overlapping/continuous contracts

• Same over_id: All contracts belonging to same continuous overlapping time period

This function provides insights into:

• Consolidation Benefits: How much employment history is simplified

• Employment Complexity: Patterns of multiple simultaneous jobs

• Administrative Efficiency: Time savings from consolidated view

• Employment Patterns: Duration and frequency of consolidated vs individual periods

Examples

if (FALSE) { # \dontrun{
library(data.table)

# Create sample employment data with overlapping periods
employment_data <- data.table(
  id = 1:8,
  cf = c(rep("PERSON001", 5), rep("PERSON002", 3)),
  INIZIO = as.Date(c("2023-01-01", "2023-03-01", "2023-03-15", "2023-06-01", "2023-08-01",
                     "2023-02-01", "2023-05-01", "2023-07-01")),
  FINE = as.Date(c("2023-02-28", "2023-03-31", "2023-05-31", "2023-07-31", "2023-12-31",
                   "2023-04-30", "2023-06-30", "2023-12-31")),
  prior = c(1, 0, 1, 1, 1, 1, 0, 1),
  company = c("CompanyA", "CompanyB", "CompanyA", "CompanyC", "CompanyD",
              "CompanyE", "CompanyF", "CompanyG"),
  salary = c(50000, 25000, 52000, 60000, 55000, 45000, 30000, 65000)
)

# Apply vecshift to get segments with over_id
segments <- vecshift(employment_data)

# Comprehensive analysis with both person and aggregate levels
analysis_full <- analyze_consolidated_periods(
  segments = segments,
  level = "both",
  include_unemployment = TRUE,
  consolidation_type = "both",
  include_details = TRUE
)

# View person-level results
print(analysis_full$person_analysis)
print(analysis_full$consolidation_benefits)

# View aggregate statistics
print(analysis_full$aggregate_analysis)
print(analysis_full$employment_complexity)

# Focus on employment consolidation only
analysis_employment <- analyze_consolidated_periods(
  segments = segments,
  level = "aggregate",
  include_unemployment = FALSE,
  consolidation_type = "employment_only",
  min_employment_duration = 7
)
print(analysis_employment$aggregate_analysis)

# Person-level analysis for detailed insights
analysis_person <- analyze_consolidated_periods(
  segments = segments,
  level = "person",
  consolidation_type = "overlapping"
)
print(analysis_person$person_analysis)

# Check analysis parameters and validation
print(attr(analysis_full, "analysis_parameters"))
print(attr(analysis_full, "validation_results"))
} # }