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Summary Summary
This course provides an introduction to the ways in which analysts measure inequality and poverty, addressing conceptual issues and practical matters including data. There are also hands-on sessions in the computer lab.
Location Location
Central London
Outline Outline
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Session |
Day 1: Monday 26 March 2018 |
Day 2: Tuesday 27 March 2018 |
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08:45–09:15 |
Registration and welcomes |
|
|
09:15–10:45 |
1. The power of pictures: description and dominance |
4. Choosing equivalence scales and poverty lines |
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10:45–11:00 |
Tea/coffee break |
Tea/coffee break |
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11:00–12:30 |
2. Indices of inequality and poverty |
5. Data issues and data sets |
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12:30–13:15 |
Lunch break |
Lunch break |
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13:15–14:45 |
3.The definition of ‘income’ |
Hands-on computer lab session |
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14:45–15:00 |
Tea/coffee break |
Tea/coffee break |
|
15:00–17:00 |
Hands-on computer lab session |
Hands-on computer lab sessions |
Learning Outcomes Outcomes
By the end of the course, participants will:
- Be able to answer questions such as: Has inequality and poverty increased in the UK? If so, by how much? Is poverty greater in the UK than in Germany?
- Be aware of the major conceptual and practical issues in distributional analysis, and the key role played by normative judgements
- Know about the nature and quality of the data sets that are available
- Gain hands-on experience of analysis of real world data, using the widely available software package Stata
- Be able to read relevant literature more critically and to apply the lessons of the course in their work
Course Details Info
Distributional comparisons and the power of pictures for (a) description and (b) normative conclusions based on dominance results: Pen’s Parade, density functions, Lorenz and generalised Lorenz curves, TIP curves. Introduction to dominance concepts and the relationship between dominance and configurations of pairs of curves.
Summary indices of inequality (percentile ratios, quantile group income shares and ratios of shares, the Gini coefficient, and Atkinson and generalised entropy indices). Summary indices of poverty (FGT, Sen etc.). Decompositions by population subgroup and by income source. The trade-off between conceptual desirability and practical issues.
Income versus consumption as measures of individual living standards or personal economic well-being. Contrasts between individual earnings, other income sources, and household income. Flows versus stocks (measures of financial wealth). Issues such as the treatment and measurement of capital gains, investment income, near-cash income (food stamps) and non-cash income (e.g. from housing, education and health, non-market production). Income sharing within households. Comparison with practice in official income distribution statistics; the trade-off between desirability and feasibility.
Adjusting observed incomes to take account of differences in household size and composition (and differences in costs of living between regions and over time). How poverty estimates vary systematically with changes in equivalence scale relativities. The choice of poverty lines for cross-national comparisons. The different approaches used in practice, including in official statistics.
Types of non-response in cross-sectional and longitudinal surveys and typical remedies (weighting and imputation). Measurement error and its implications for estimates of inequality and poverty. Administrative data versus household survey data: relative advantages and disadvantages.
A basic introduction to sampling variability with simple random samples and clustered and stratified sample designs, and statistical inference for poverty and inequality comparisons
Datasets for distributional analysis, including UK unit record datasets such as FRS/HBAI, LCFS/ETB, SPI, WAS, and their international counterparts, and LIS. Collections of estimates including e.g. UK, DWP, IFS for UK, and internationally (WIID, World Bank, OECD, Eurostat).
Analysis primarily based application of Stata to the materials (including data) downloadable from https://econpapers.repec.org/paper/bocasug06/16.htm: this includes some UK unit record data (essentially ONS/ETB data for 1981, 1986, 1991) and refers to a publicly-available suite of Stata programs written by SPJ.
Lab work could also include participants’ analysis of (a) data downloaded from collections of estimates, e.g. national trends and cross-national comparisons; and/or (b) participants’ own datasets from whatever project they might be working on