Bibliographic record
Abstract
The present thesis analyses the evolution and accumulation of national wealth, from a cross-country perspective. It is composed of three different chapters. In chapter one, together with Miguel Artola Blanco and Clara Martínez-Toledano, we reconstruct Spain’s national wealth from 1900 to 2017. Combining new sources with existing accounts, we estimate the wealth of both private and government sectors and use a new asset-specific decomposition of the long-run accumulation of wealth. We find that the national wealth to income ratio stood during the 20th century in a relatively close range -between 400 and 600%- until the housing boom of the early 2000s led to an unprecedented rise to 800% in 2007. Our results highlight the importance of land, housing capital gains and international capital flows as key elements in the accumulation of wealth. The second chapter investigates the connection between the striking rise of national housing-to-national income ratios experienced by rich countries over the last decades with the transformation of the productive system: from manufacturing to services. It explores two factors: the increase in countries’ spatial concentration of economic activity and the negative shocks to manufacturing-specialized regions. To explore the first factor, I present new series of housing wealth and of spatial concentration of economic activity in seven developed economies: France, Germany, Italy, Japan, Spain, UK and USA. Results show that rising housing wealth is the consequence of higher urban land values, with this increase being tightly connected with larger concentration of market-oriented services. The second factor is investigated using urban-level data in England and Wales, thus analyzing how macro trends in house-income ratios emerge from the local level. I find that rising national values are largely the result of higher dispersion of local house prices, but not of incomes per capita. The best predictor of how house prices changed is the city-level specialization of the productive system. I estimate that one quarter of the dispersion in local house prices is explained by manufacturing output declining at the national level. A simple theoretical model is used to rationalize these two factors. The third chapter, presents updated series of national wealth and capital shares of income for the eight countries covered by Piketty and Zucman (2014a): Australia, Canada, France, Germany, Italy, Japan, the UK and the USA. It discusses the adaptation of the series from the SNA93 to the SNA2008, the inclusion of natural capital (i.e. forestry land, mineral and energy resources) within the concept of national wealth and the division of national housing across households and other sectors. I find that adopting the SNA2008 has no relevant consequences for aggregate macro wealth or for the net-of-depreciation capital share. However, gross-of-depreciation capital shares are higher, likely due to the inclusion of R&D as investment in the new system of accounts. Overall, new series reveal that average private wealth to national income ratios have been steadily increasing in recent years with capital-labor shares remaining relatively constant at their 2010 values.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".