The Relationship between the Resource Curse and Genuine Savings as an Indicator for Weak Sustainability : Theoretical Background and Empirical Evidence
Bibliographic record
Abstract
This dissertation deals with the relationship between the Resource Curse (RC) – the empirically proven paradox that countries with abundant natural resources often show slower economic growth – and a measure for the substitution of natural resource depletion by other forms of capital, the World Bank’s Genuine Savings (GS). In four logically successive parts, which are all published papers, this work analyzes the “alarming picture [that most] [c]ountries with a large percentage of mineral and energy rents of GNI typically have lower genuine saving rates” (VAN DER PLOEG 2011: 396-397) as well as its background determinants and transmission channels. The first part establishes a critical survey on GS and its calculation components and is intended as an introduction to its theoretical and methodical background. The WORLD BANK (2011) adjusts Net National Savings for human and natural capital, but as an indicator of real world sustainability GS has several shortcomings, which are discussed at length. For example, there are natural resources that are omitted due to empirical and methodological reasons such as fisheries, biodiversity or diamonds. Part one discusses possible extensions and ideas for future development, but the most important finding is the necessity for more analyses of possible factors determining the development of GS rates. With this background, part two establishes a theoretical model of the relationship between the RC and GS. Since both the RC and GS depend on the amount of resource depletion and exports, a possible relation seems clear. Therefore, part two uses the exogenous and endogenous explanations from countless studies contributing to the research on the RC and its determinants and relates them to GS and its calculation components. For example, the volatility of international commodity markets affects the natural resource rents within the calculation of GS immensely, or the migration of employees to the resource sector has a clear impact on the education expenditures used to determine human capital. In part three, these findings and the resulting theoretical model are used to show the empirical relationships between the RC and GS in cross-country regressions. For example, the mentioned terms-of-trade volatility in resource-dependent countries affects the calculation of GS on multiple levels. Overall, results show that factors leading to the RC are also useful explanatory variables for GS. Hence, part three of this dissertation shows that the theoretical framework from part two holds true in comprehensive cross-country regressions with a variety of dependent and independent variables. To complete the analysis, part four examines Zambia as a case study of a RC-affected country. Between 1964 and 2012 Zambia depended on copper exports at an average 33% of its GNI and suffered a decline of its real per capita income in the same period, and importantly showed an average GS rate of -3%. The study demonstrates that most of the theories relating the RC to GS apply to the Zambian situation. Its GS developed with high volatility and completely in line with world copper prices and to a lesser but not negligible extent with political developments. Following parts two and three, this case study completes the picture on the deep relationship between the RC and GS and closes the loop of a theoretical model with cross-sectional empirical research as well as a research design which allows for a more qualitative discussion.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".