Unbundling Institutions: Using Economic Analysis to Understand the Importance of Institutions
Bibliographic record
Abstract
A large literature on institutions has developed that claims that institutions influence long-run growth and development; thus, it becomes critical to carefully examine what institutions influence which outcomes to better understand factors that influence long-run economic growth across countries. Douglass North terms institutions as the rules of the game and this paper will work to further examine these rules by looking at private property rights institutions, which are the rules and regulation protecting citizens against the power of the government and elites , and contracting institutions, which are the rules and regulations governing contracts between ordinary citizens. The paper Unbundling Institutions , published in the Journal of Political Economy by Daron Acemoglu and Simon Johnson in 2005, seeks to conceptualize the different factors within the institutional framework and provide some semblance of which of these factors provide the most relevant analysis. The paper finds that property rights institutions have a strong influence on long-run economic growth, investment, and financial development, while contracting institutions have a more limited impact on those same factors. My presentation will give a concise background on the development of the new institutional approach and explain the reasoning for the conceptual divide between property rights and contracting institutions. Using a two-stage least squares regression (2SLS) approach and the instrumental variable approach, the presentation will address issues of causality and correlation. Identifying the link to the policy arena will provide context as to why this area is of particular importance.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".