Recessions and Recoveries in New Zealand's Post-Second World War Business Cycles
Bibliographic record
Abstract
We compute classical real GDP business cycles and growth cycles, contrast classical recessions with 'technical' recessions, and assess the sensitivity of our peaks and troughs to data revisions. Calling a technical recession after two successive quarters of negative growth can provide conditionally useful information. However, it can also signal beginning and end points for a recession that are somewhat different from those computed by our Bry and Boschan algorithm. Expansion and contraction phases of classical real GDP and employment cycles have, on average, had an 89% association, but individual cycle circumstances should additionally be assessed. New Zealand's average pattern of recovery has differed from that for U.S. NBER cycles, but their most recent recession and recovery paths have been unusually similar. We also assess whether strength of recovery can be explained by length, depth or severity of previous recessions. From our classical real GDP turning points, New Zealand's most recent recession commenced with the March 2008 quarter and ended with the June 2009 quarter. The duration of this six-quarter recession has been somewhat longer than the average recession of 4.3 quarters; but its 4.0 percentage depth has been considerably less than those for the 1951/52 and 1948 recessions, somewhat below that for the 1976/78 episode, and marginally less than the average depth of 4.1 per cent. In terms of overall severity, a measure which reflects duration and depth, this recession has been New Zealand’s fourth most severe. Its cumulated GDP loss of 11.5 per cent has been greater than the average loss of 10.4 per cent, but less severe than the losses for 1951/52 (37.2 per cent), 1948 (15.6 per cent) and 1976/78 (12.8 per cent). The recovery path from New Zealand’s most recent recession has differed from those of previous recoveries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".