Firm-level innovation in New Zealand
Bibliographic record
Abstract
Innovation is an issue that has attracted considerable research interest in economics. Innovation related data, collected via firm based surveys, has become the norm for many countries (e.g. Canada, United States, Malaysia, Taiwan, Australia). In New Zealand the main survey instrument of this type is the Business Operation Survey (BOS), which is an integrated, modular survey developed by Statistics New Zealand (SNZ). The survey has been operating annually since 2005 and includes up to three "modules" each with its own specific objectives. The first module focuses on business performance and the characteristics of participating firms. The longitudinal dimension of these data enables changes over time to be analyzed and facilitates the investigation of causal relationships. The second module operates on a rotational basis, where the survey content alternates between innovation and business use of Information and Communication Technology (ICT). The innovation module replaced the Innovation Survey, which was last run in 2003. This paper seeks to identify the innovative behaviour of New Zealand firms using the Longitudinal Database (LBD) that stems from the 2006 SNZ IBULDD (Improved Business Understanding via Longitudinal Database Development) initiative. IBULDD links business related data (including BOS) into an integrated longitudinal database. Starting from a detailed review of the international innovation research literature, a list of potential regression variables was established. A new set of probit regression models are proposed where four different innovation outcomes were developed and tested in an attempt observe the stability of the models over time. In summary the results of presented in the paper are: Firstly, New Zealand firms appear to experience smaller positive size and market power effects than found internationally due to the unique (micro-sized) firm demographics. The large impact of SMEs and the relatively flat market structure appear to have disadvantaged individual businesses in the innovation space as well as potentially New Zealand as a whole. Secondly, general investment may be more beneficial than specific R&D projects. R&D projects generally require large quantities of resources from participants, and the pay-off periods tend to be longer. Without sufficient economies of scale it is extremely risky for firms to participate. In contrast, small scale investments aimed at technology acquisition, product improvements and market entry appear to be more cost effective options in the short run. Exporting and direct investment overseas are two preferred channels for seeking market information and innovation opportunities. Finally, favorable regional environments are innovation enhancing, however once an acceptable level has been reached diminishing marginal returns appear to set in quite quickly. From a policy prospective, it seems necessary to alter the policy setting in response to the current market environment and over-investment in infrastructure is not recommended, given resource constraints and potential opportunity costs.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".