Compute Core HRMIS Analytical Tables — compute_hrmreport

This function generates a suite of standardized analytical tables for HRMIS (Human Resource Management Information System) reports. It combines contract-level, personnel-level, and establishmental data to compute wage bill summaries, employment shares, decompositions, and profiles by occupation, pay grade, establishment, education, and seniority.

Usage

compute_hrmreport_stats(contract_dt, personnel_dt, est_dt, macro_indicators)

Arguments

contract_dt: A `data.table` containing individual employment contracts with variables such as `personnel_id`, `ref_date`, wage variables (`gross_salary_lcu`, `net_salary_lcu`, `base_salary_lcu`), and job attributes.
personnel_dt: A `data.table` containing personnel-level panel data, including `personnel_id`, `ref_date`, demographic and employment information.
est_dt: A `data.table` containing establishmental information (e.g., institution identifiers, types, or sectors).
macro_indicators: A data frame containing macro indicators.

Value

A named list of `data.table` objects containing:

wagebill_shares: Wage bill components as shares of macro indicators.
publicemployment_share: Public employment as a share of total employment.
wagebill_occupisco: Wage bill decomposition by ISCO occupation group.
wagebill_occupnative: Wage bill decomposition by native occupational titles.
wagebill_estdecomp: Wage bill decomposition by establishment.
wagebill_allowshare_paygrade: Allowance rate by pay grade.
wagebill_allowshare_seniority: Allowance rate by seniority.
personnelevent: Personnel-level hiring, firing, and retirement events over time.
employment_decomp: Employment decomposition by occupation and ISCO group.
est_decomp: Employment decomposition by establishment.
education_profile: Distribution of public sector personnel by education, gender, and occupation.
mobilityprofile: Distribution of public sector personnel by pay grade, seniority, gender, and occupation.

Details

The function integrates contract, personnel, and establishmental datasets to compute a standardized HRMIS statistical report. It relies on supporting helper functions such as: convert_constant_ppp(), compute_fastshare(), compute_fastsummary(), detect_personnel_event(), and detect_retirement().

Each sub-table in the output list can be used directly in dashboards, reports, or further analytical aggregation.

Examples

if (FALSE) { # \dontrun{
hrm_stats <- compute_hrmreport_stats(contract_dt = contract_data,
                                     personnel_dt = personnel_data,
                                     est_dt = est_data)
names(hrm_stats)
} # }