Bibliographic record
Abstract
Austerity is a term used to describe debt-reduction policies, but it can mean radically different things. For some people, austerity means adopting a debt-reduction package dominated by tax increases. For others, it means adopting a package made mainly of spending restraint--including reforms of social programs. The lack of a distinction between two meanings of word--and hence, distinction between two different debt-reduction policies--is unfortunate and could also explain confusion over what is happening in Europe. In this debate there are two important questions to keep in mind. The first question asks, Which of two types of austerity measures successfully reduces debt-to-GDP ratio? The second asks, What is impact of austerity measures on economic growth? Which of Two Types of Austerity Measures Successfully Reduces Debt to GDP? The United States is not first nation to struggle with a worrisome debt-to-GDP ratio. Fortunately, academic world has already produced great insights into what can be done to help problem without hurting economy. Take Harvard University economists Alberto Alesina and Silvia Ardagna. In an October 2009 working paper published by National Bureau of Economic Research, duo look at 107 efforts to reduce debt in 21 OECD nations between 1970 and 9.007. Several countries were successful, among them Austria in 2005, Finland in 2005, and Sweden from 1997 to 9,004. Spending cuts, scholars found, are more effective than tax increases in reducing ratio of debt to GDP. With successful fiscal adjustments, spending as a share of GDP fell by an average of 2 percentage points while revenue fell by half a percentage point. Unsuccessful fiscal-adjustment packages involved smaller spending reductions (only about eight-tenths of a percentage point, on average) and large revenue increases. Following and building on work of Alesina and Ardagna (2009), American Enterprise Institute economists Andrew Biggs, Kevin Hassett, and Matthew Jensen published a working paper in December 2010 covering more than 100 instances in which countries took steps to address their budget gaps. They identify successful consolidations as those in which ratio of debt to potential GDP three years following first yea of consolidation declined by at least 4.5 percentage points. Their conclusion: Countries that addressed their budget shortfalls through reduced spending burdens were far more likely to reduce their debt than countries whose budget-balancing strategies depended upon higher taxes. What's more, the typical unsuccessful fiscal consolidation consisted of 53 percent tax increases and 47 percent spending By contrast, typical successful fiscal consolidation consisted of 85 percent spending cuts. These results are extremely mainstream. My colleague at Mercatus Center Matt Mitchell has done a review of academic literature on this issue and he finds of 22 papers published that looked at this question all of them find that most promising way to shrink file debt is to restrain spending so it shrinks relative to economic output and not to increase taxes (Mitchell 2011). But there are other factors worth mentioning when talking about successful fiscal adjustments. Looking at 66 instances of fiscal adjustments in Canada, France, United States, Japan, Germany, and Italy, authors of IMF book called Chipping Await at Our Debt, find that ambitious plans tend to produce more adjustments than modest ones (Mauro 2011). They also find also that such plans aren't associated with more frequent changes in government (in other words, politicians who adopt ambitious fiscal adjustment plans aren't penalized by voters). However, book does stress fact that public support is a key factor to achieving successful fiscal adjustment. Interestingly, successful fiscal adjustments are rooted in reform of social programs and reduce size and pay of government work force. …
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".