dorsal/arxiv
View SchemaEmpirical study and model of personal income
| Authors | Wataru Souma, Makoto Nirei |
|---|---|
| Categories | |
| ArXiv ID | physics/0505173 |
| URL | https://arxiv.org/abs/physics/0505173 |
Abstract
Personal income distributions in Japan are analyzed empirically and a simple stochastic model of the income process is proposed. Based on empirical facts, we propose a minimal two-factor model. Our model of personal income consists of an asset accumulation process and a wage process. We show that these simple processes can successfully reproduce the empirical distribution of income. In particular, the model can reproduce the particular transition of the distribution shape from the middle part to the tail part. This model also allows us to derive the tail exponent of the distribution analytically.
{
"annotation_id": "067295c4-f322-4ac7-b291-9084a3baa5f7",
"date_created": "2026-03-02T18:00:56.877000Z",
"date_modified": "2026-03-02T18:00:56.877000Z",
"file_hash": "a069386e54d20c2aa498e31e4962fec55736febbfe90694d7c0c9bb5275dd95c",
"private": false,
"record": {
"abstract": "Personal income distributions in Japan are analyzed empirically and a simple\nstochastic model of the income process is proposed. Based on empirical facts,\nwe propose a minimal two-factor model. Our model of personal income consists of\nan asset accumulation process and a wage process. We show that these simple\nprocesses can successfully reproduce the empirical distribution of income. In\nparticular, the model can reproduce the particular transition of the\ndistribution shape from the middle part to the tail part. This model also\nallows us to derive the tail exponent of the distribution analytically.",
"arxiv_id": "physics/0505173",
"authors": [
"Wataru Souma",
"Makoto Nirei"
],
"categories": [
"physics.soc-ph",
"cond-mat.stat-mech",
"q-fin.GN"
],
"title": "Empirical study and model of personal income",
"url": "https://arxiv.org/abs/physics/0505173"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "036fc00f-1e85-4eff-9593-8c8af33e3552",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}