From fbd104286d9e3027435bf9f61b550213e1b171b2 Mon Sep 17 00:00:00 2001 From: Vahid Ahmadi Date: Wed, 18 Dec 2024 15:39:02 +0100 Subject: [PATCH] write data and preprocessing --- docs/LA_methodology.ipynb | 872 ++++++++++++++++++ docs/pictures/nomis_screenshot2.png | Bin 0 -> 303191 bytes .../targets/nomis_earning_jobs_data.xlsx | Bin 0 -> 47741 bytes 3 files changed, 872 insertions(+) create mode 100644 docs/LA_methodology.ipynb create mode 100644 docs/pictures/nomis_screenshot2.png create mode 100644 policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/nomis_earning_jobs_data.xlsx diff --git a/docs/LA_methodology.ipynb b/docs/LA_methodology.ipynb new file mode 100644 index 0000000..6c81703 --- /dev/null +++ b/docs/LA_methodology.ipynb @@ -0,0 +1,872 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Local authority methodology" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "When policy changes in the UK - taxes, benefits, or public spending - it affects places and people differently. PolicyEngine UK builds tools to analyze incomes, jobs, and population patterns in each local authoirity district. This documentation explains how we create a microsimulation model that works at the local authoirity level. The system combines workplace surveys of jobs and earnings, HMRC tax records, and population statistics. We estimate income distributions, and optimize geographic weights.\n", + "\n", + "This guide shows how to use PolicyEngine UK for local authoirity analysis. We start with data collection, transform it for modeling, and build tools to examine policies. The guide provides examples and code to implement these methods. Users can measure changes in household budgets, track employment, and understand economic patterns on different local authoirity districts. This document starts with data collection from workplace surveys, tax records, and population counts, then explains how we convert this data into usable forms through income brackets. It concludes with technical details about accuracy measurement and calibration, plus example code for analysis and visualization." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data\n", + "\n", + "In this section, we describe three main data sources that form the foundation of our local authority level analysis: earning and jobs data from NOMIS ASHE, income statistics from HMRC, and population age distributions from the House of Commons Library.\n", + "\n", + "### Earning and jobs data\n", + "\n", + "Data is extracted from NOMIS Annual Survey of Hours and Earnings (ASHE) - workplace analysis dataset, containing number of jobs and earnings percentiles for all UK local authority districts from the [NOMIS website](https://www.nomisweb.co.uk/datasets/ashe). This dataset is stored as [`nomis_earning_jobs_data.xlsx`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/nomis_earning_jobs_data.xlsx). To download the data, follow the variable selection process shown in the image below:\n", + "\n", + "![](pictures/nomis_screenshot2.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Income data\n", + "\n", + "Income data for UK local authorities is obtained from [HMRC](https://www.gov.uk/government/statistics/income-and-tax-by-borough-and-district-or-unitary-authority-confidence-intervals). This dataset provides detailed information about income and tax by local authorities with confidence intervals, and is stored as [`total_income.csv`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/total_income.csv), including two key variables:\n", + "\n", + "- [`total_income_count`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/total_income.csv#L1): the total number of taxpayers in each local authority\n", + "\n", + "- [`total_income_amount`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/total_income.csv#L1): the total amount of income for all taxpayers in each local authority\n", + "\n", + "We use these measures to identify similar local authorities when employment distribution data is missing. Our approach assumes that local authorities with similar income patterns (measured by both taxpayer counts and total income) will have similar earnings distributions. The following table shows the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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First, we estimate the full distribution of earnings by:\n", + " - Using known percentile data (10th to 90th) from the ASHE dataset\n", + " - Extending this to estimate the 90th-99th percentiles using ratios derived from [this government statistics report](https://www.gov.uk/government/statistics/percentile-points-from-1-to-99-for-total-income-before-and-after-tax#:~:text=Details,in%20the%20Background%20Quality%20Report)\n", + "\n", + "2. This estimation allows us to map earnings data into brackets that align with policy thresholds.\n", + "\n", + "After estimating the full earnings distribution, we convert the data into income brackets. We calculate the number of jobs and total earnings for each local authority and income bracket based on the estimated earnings distribution. When we encounter local authorities with missing data, we estimate their earnings distribution pattern using data from local authorities with similar total number of taxpayers and total income levels. \n", + "\n", + "The Python script [`create_employment_incomes.py`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/create_employment_incomes.py) generates [`employment_income.csv`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/employment_income.csv) containing number of jobs ([`employment_income_count`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/employment_income.csv#L1)) and total earnings ([`employment_income_amount`](https://github.com/PolicyEngine/policyengine-uk-data/blob/7c782c4839a024c729350a3ff2c76922bf21b0d4/policyengine_uk_data/datasets/frs/local_areas/local_authorities/targets/employment_income.csv#L1)) for each constituency and income bracket. The following table shows employment and income across different brackets for constituencies:" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/pictures/nomis_screenshot2.png b/docs/pictures/nomis_screenshot2.png new file mode 100644 index 0000000000000000000000000000000000000000..aab0680df466e0a75b315aed806819e8642c28c5 GIT binary patch literal 303191 zcmb5VcUV)+);CN?LBIkcAVoz)dJnybfG9`+fI6_r9*Z_he?)OlH=sHEVur?T;^YH0WqpX-G&&=ro@_ zeMLe-6HP)w$#{i|n6v#RN1TM@nvRpI>Pt;kRj!wwAbTfQI}(y-9}|qJU+e$AooV(; z^UC!PA>W46xb8m;`F7ouWL@og!e@#r4}0EcztLc3GH?E1t!AuZ?hU4VTWw=}yL-#@ zHZ2W5cCl$H0dOxwh|qV0Az%iBFJ<AgK4aZL< 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