statskita 0.3.0

Python toolkit for Indonesian official microdata (SAKERNAS, SUSENAS)

StatsKita: Python toolkit for Indonesian official microdata (SAKERNAS, SUSENAS)

v0.3.0: SAKERNAS (labor) + SUSENAS (poverty/inequality)

What's Supported

Dataset	Waves	Indicators
SAKERNAS	5 (2023-02 → 2025-02)	Labor force, employment, wages
SUSENAS	2 (2023-03, 2024-03)	Poverty (P0, P1, P2), Gini

Installation

pip install statskita

Quick Start

Single wave:

import statskita as sk

# SAKERNAS
df = sk.load_sakernas(wave="2025-02")
clean_df = sk.wrangle(df, harmonize=True, source_wave="2025-02")
design = sk.declare_survey(clean_df, weight="survey_weight", psu="psu")
results = sk.calculate_indicators(design, ["unemployment_rate", "lfpr"])

# SUSENAS
df = sk.load_susenas(wave="2024-03", module="kp", category="housing")
design = sk.declare_survey(df, weight="WEIND", wave="2024-03")
results = sk.calculate_indicators(design, ["p0", "p1", "p2", "gini"])

Multi-wave comparison:

# SAKERNAS
sakernas_waves = {w: sk.load_sakernas(wave=w) for w in ["2023-02", "2023-08", "2024-02", "2025-02"]}
harmonized = {w: sk.wrangle(df, harmonize=True, source_wave=w) for w, df in sakernas_waves.items()}
sakernas_results = sk.calculate_indicators_multi(harmonized, "all", as_wide=True)

# SUSENAS
susenas_waves = {w: sk.load_susenas(wave=w, module="kp", category="housing") for w in ["2023-03", "2024-03"]}
susenas_results = sk.calculate_indicators_multi(susenas_waves, ["p0", "p1", "p2", "gini"], as_wide=True)

SAKERNAS output:

┌─────────────────────────────────┬──────┬─────────┬─────────┬─────────┬─────────┐
│ indicator                       ┆ unit ┆ 2023-02 ┆ 2023-08 ┆ 2024-02 ┆ 2025-02 │
├─────────────────────────────────┼──────┼─────────┼─────────┼─────────┼─────────┤
│ labor_force_participation_rate  ┆ %    ┆ ...     ┆ ...     ┆ ...     ┆ ...     │
│ employment_rate                 ┆ %    ┆ ...     ┆ ...     ┆ ...     ┆ ...     │
│ unemployment_rate               ┆ %    ┆ 5.45    ┆ 5.32    ┆ 4.82    ┆ 4.76    │
│ underemployment_rate            ┆ %    ┆ ...     ┆ ...     ┆ ...     ┆ ...     │
│ female_lfpr                     ┆ %    ┆ ...     ┆ ...     ┆ ...     ┆ ...     │
│ average_wage                    ┆ M Rp ┆ ...     ┆ ...     ┆ ...     ┆ ...     │
│ neet_rate                       ┆ %    ┆ ...     ┆ ...     ┆ ...     ┆ ...     │
└─────────────────────────────────┴──────┴─────────┴─────────┴─────────┴─────────┘

SUSENAS output:

┌───────────┬──────┬─────────┬─────────┐
│ indicator ┆ unit ┆ 2023-03 ┆ 2024-03 │
├───────────┼──────┼─────────┼─────────┤
│ p0        ┆      ┆ 9.36    ┆ 9.03    │
│ p1        ┆      ┆ 1.53    ┆ 1.46    │
│ p2        ┆      ┆ 0.38    ┆ 0.35    │
│ gini      ┆      ┆ 0.39    ┆ 0.38    │
└───────────┴──────┴─────────┴─────────┘

Features

• Multi-wave analysis: Built-in cross-wave comparison
• Survey-aware: Handles weights, strata, PSU correctly
• Open source: Free, no licensing costs

See examples/ for detailed usage.