Comment on “Shares of the Rich and the Rest in the World Economy: Income Divergence Between Nations, 1820–2030”
Why this work is in the frame
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Bibliographic record
Abstract
Maddison (2008) provides a millennial review of national income trends, focusing on the period since 1820, makes forecasts to 2030, roughly 25 years hence, and compares these projections to those obtained from other sources. The forecasts imply a number of outcomes that readers may find interesting, such as China becoming the world's largest economy by 2015, and per capita incomes in the USA exceeding those in Japan and Western Europe by 50% in 2030. When evaluating this sort of exercise, it is important to keep in mind that even over relatively short horizons of say 25 years, the fortunes of individual countries can exhibit significant unexpected divergences. At the turn of the 20th century, per capita incomes were similar in the land-abundant grain producers Argentina and Canada, and incomes in Argentina topped those in Canada as recently as 1934. But 25 years hence, Canada's per capita income exceeded Argentina's by 65 percent, and by 2000 the gap had grown to 160 percent. In the immediate postwar period, Burma and the Philippines were pegged as Asia's rising stars. The Philippines, at least, was not such a bad bet: the country had the world's second highest ratio of human capital to contemporaneous income (trailing Japan but exceeding South Korea). In 1975, per capita incomes in the Philippines exceeded those in Thailand, but 25 years later, Thailand topped the Philippines by nearly 165 percent –despite the 1997 financial crisis that had a disproportionate impact on Thailand. In the 1960s, it is unlikely that anyone contemplating the futures of North and South Korea would have imagined that 25 years hence the poorer South would be preparing to join the Organisation for Economic Cooperation and Development, while the richer North would be on the precipice of one of the worst famines of the 20th century. The titles of two popular books on Japan's economy, Japan As Number One: Lessons for America (Vogel, 1979) and The Sun Also Sets: The Limits to Japan's Economic Power (Emmott, 1989) nicely illustrate this phenomenon with respect to our host's economy. These observations are raised not to belittle to this exercise, but rather to underline how the specific forecasts ought to be taken with the appropriate grains of salt, something that Professor Maddison acknowledges. While the national-level forecasts provide considerable food for thought, much recent attention to inequality has focused on within-country inequality where the relevant policy instruments are better developed. There are multiple hypotheses as to the drivers. These include Stolper–Samuelson effects generated by the effective increase in world labor supply in recent decades associated with some large countries integrating into the global economy, abetted by technological progress which has reduced transactions costs over long distances. Increased cross-border capital mobility has made it more difficult to tax capital relative to labor and may have contributed to a rise in inequality. Technological change may have also both changed relative returns to certain personal skills, aptitudes, or characteristics, reducing the returns to doing routinized tasks relative to interpersonal skills and specific technical aptitude, and even the actual structure of work and management, including the flattening of management, making it possible for a small number of highly placed decision-makers to appropriate the rents generated by large corporate entities. Lastly, increased transborder labor migration is alleged to have deepened inequality within some countries such as the USA. Consideration of the migration issue brings us back full circle to the underlying forecasts. The assumption of constant rates of migration will almost certainly prove incorrect – if cross-country inequality grows as forecast in Table 7, then there will be increasing pressure for migration, much of it illegal. Demographics may emerge as another significant driver as well: rapidly aging countries like Japan and South Korea may face a variety of internal demands and pressures to relax immigration restrictions. Such stresses may prove difficult to resolve politically. It goes without saying that the migration issue is an increasingly contentious one within many countries. But one of the striking characteristics of the world economy today is that while there are reasonably well-developed multilateral norms and institutions with respect to the cross-border flow of goods and capital, there is no equivalent of the World Trade Organization for the movement of people. Uncertainty about policy both at the national and international levels make it difficult to project out to 2030 what the pattern and magnitude of cross-border migration will be, but it is plausible that the divergences from the assumptions underlying this paper's projections may be sufficiently large to alter the forecasts appreciably, at least at the level of some individual countries.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it