Survey of Indigenous Firms: A Snapshot of Wages, Prices and Financing in the Indigenous Business Sector in Canada
Bibliographic record
Abstract
Attempts to measure and track the Indigenous economy in Canada are limited by data availability and quality. Also, little is known about the business environment on reserves. Filling these information gaps is important to ensure that policy-makers and Indigenous leaders can make well-informed decisions that benefit the long-term prosperity of Indigenous communities. To help narrow these knowledge gaps, the Bank of Canada partnered with the Canadian Council for Aboriginal Business and Global Affairs Canada to conduct a large-scale, national survey of Indigenous-owned firms between May and September 2021. This paper reports findings from the survey results, including Indigenous-owned firms’ main sources of financing and their expectations about wages, prices and inflation. These results are compared with those from other Canadian business surveys such as the Bank’s quarterly Business Outlook Survey (BOS) to better understand the unique conditions and challenges Indigenous businesses face. Overall, we find that, compared with the average small business in Canada, Indigenous firms were significantly less likely to use financial institutions as main sources of financing. Indigenous businesses also had stronger inflation expectations and weaker wage-growth expectations, on average, than non-Indigenous firms in Canada, based on results from the BOS during the same time frame. The relatively high inflation expectations partly reflect the large share of Indigenous firms located in rural areas compared with the total business population in Canada. Indigenous firms in rural locations tended to expect higher inflation and higher price increases than their counterparts in urban areas.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".