Trends of Poverty and Income Inequality in Cross-National Comparison
Bibliographic record
Abstract
Comparative research of poverty, income inequality and the effectiveness of income transfer systems has flourished during the last two decades, largely owing to the contribution of the Luxembourg Income Study project. So far, however, the majority of comparative analyses have been based on a single year. For this paper we analyzed cross-national patterns of poverty and income inequality with a special emphasis on their stability. We studied trends of poverty and income inequality between 1980 and 1995 in nine countries representing three different ideal types of social policy. The differences in poverty across the countries studied corresponded with the respective models of social policy more clearly in the mid-1990s than they did 15 years earlier. Generally speaking, the poverty rate is slightly under 5% in the Nordic countries, around 7.5% in Central Europe, 10% in Canada, 12.5% in the UK, and as high as 17.5% in the USA. All the countries included in the analysis share the trend that the primary distribution - based on the market income - has become less equal than before. In each country, the proportion of population being able to gain subsistence from the market alone has decreased continuously. This trend is significantly more remarkable than the change in actual poverty, which means that the absolute poverty alleviating impact of the income redistribution systems became stronger in these countries during the period 1980-1995. The analysis of income inequality produced a basically similar picture of the differences across the countries and the models of social policy as the analysis of poverty did. In comparison to poverty, however, the change is generally speaking less extensive. The Nordic countries, in particular, have been capable of responding to the rise of the market income differences so that the income inequality for disposable incomes has practically not increased at all. Canada shows a parallel trend. The USA and, in particular, the UK represent the opposite development. We also analyzed trends of poverty in various population groups. It was found that by 1995 poverty had turned into a risk of young adults in all the countries studied. The poverty rate increased for the age group 18-30 years in all countries, while an opposite trend was observed among the elderly, in particular those aged over 65. Poverty rate among the elderly is nowadays below the average population-level rate in all the countries studied.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".