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Record W2733086276 · doi:10.55016/ojs/sppp.v10i1.42626

Policy Interventions Favouring Small Business: Rationales, Results and Recommendations

2017· article· en· W2733086276 on OpenAlexaffabout
John Lester

Bibliographic record

VenueThe School of Public Policy Publications · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsUniversity of Calgary
FundersAustralian Government
KeywordsPsychological interventionBusinessPublic economicsEconomicsMedicineNursing

Abstract

fetched live from OpenAlex

Small business has a well-deserved reputation as the driver of job growth and as a key contributor to innovation. In the 12 years ending in 2013, small and medium-sized enterprises (SMEs) accounted for about 90% of private sector job growth in Canada. What is less well-recognized, however, is that a small fraction of SMEs account for most of the job growth and innovation. As a result, governments have offered broad-based support for small businesses, rather than focusing on high-impact entrepreneurs. This approach is wasteful: firms that do not grow or innovate receive most of the benefits. Further, this approach can harm economic performance by promoting the expansion of smaller, lessefficient firms at the expense of larger ones. The federal government elected in 2015 is focussing new initiatives on innovative and growth-oriented businesses. Legislated reductions in the small business tax rate were reversed and targeted support for innovative SMEs was increased. While the change in direction is welcome, almost 85% of the $7 billion yearly funding for small business continues to provide broad-based support. The largest program is the special low rate of tax for small businesses, implemented to improve access to financing for capacity-expanding investment. This measure is harming economic performance because the cost of shifting capital and labour from large to smaller, less-efficient businesses outweighs the benefit from improving access to capital. Large subsidies for small business financing are also provided by the Business Development Bank of Canada (BDC). With access to cheap government funding, the BDC is profitable, but evaluated using a more realistic cost of financing, the bank operates at a substantial loss. This loss exceeds the benefit from improving access to capital, particularly for the bank’s direct-lending program. While there is a solid argument for supporting R&D, subsidies provided to small firms are so generous that they are harming economic performance. The federal government provides a 35% tax credit for R&D performed by small firms. Provincial tax credits raise the subsidy rate to about 42%. And those firms receiving support from the federal Industrial Research Assistance Program can have almost 60% of their project costs paid by the government. By way of contrast, large firms performing R&D receive subsidies from federal and provincial tax credits amounting to under a quarter of their costs, an intervention which improves economic performance. Canada has had what could be described as a small business policy – broad-based support for all small businesses. The newish federal government is moving to an entrepreneurship policy: new initiatives emphasize support for the high-impact firms and individuals that make an outsized contribution to Canada’s innovation and prosperity. Making the transition to the new framework will require overhauling legacy small business policies to free up resources for new initiatives and to secure fiscal savings. Three changes would pay big dividends: • Eliminate the small-business corporate income tax deduction. • Reduce the enhanced R&D tax credit rate to the same level as the regular credit. • Replace the BDC’s direct loan program with a loan guarantee program.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.149
GPT teacher head0.316
Teacher spread0.167 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2017
Admission routes2
Has abstractyes

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