Gender-responsive public procurement: strategies to support women-owned enterprises
Bibliographic record
Abstract
Purpose This paper aims to inform strategies to enhance public procurement opportunities for women-owned small- and medium-sized enterprises (SMEs). To do so, the study examines two research questions: To what extent are women-owned enterprises under-represented among SME suppliers to government; and Do barriers to public procurement – as perceived by SME owners – differ across gender? Design/methodology/approach The study draws on the resource-based view (RBV) of the firm and on theories of role congruity and social feminism to develop the study’s hypotheses. Empirical analyses rely on comparisons of a sample of 1,021 SMEs that had been suppliers to government and 9,376 employer firms that had not been suppliers to government. Data were collected by Statistics Canada and are nationally representative. Logistic regression analysis was used to control for systemic firm and owner differences. Findings Controlling firm and owner attributes, majority women-owned businesses were underrepresented as SME suppliers to government in some, but not all sectors. Women-owned SMEs in Wholesale and Retail and in Other Services were, ceteris paribus, half as likely as to be government suppliers as counterpart SMEs owned by men. Among Goods Producers and for Professional, Scientific and Technical Services SMEs, there were no significant gender differences in the propensity to supply the federal government. “Complexity of the contracting process” and “difficulty finding contract opportunities” were the obstacles to contracting cited most frequently. Research limitations/implications The limitations of using secondary analyses of data are well documented and apply here. The findings reflect only the perspectives of “successful bidders” and do not capture SMEs that submitted bids but were not successful. Furthermore, the survey did not include questions about sub-contractor enterprises, data that would likely provide even more insights about SMEs in government supply chains. Accordingly, the study could not address sub-contracting strategies to increase the number of women-owned businesses on government contracts. Statistics Canada’s privacy protocols also limited the extent to which the research team could examine sub-groups of small business owners, such as visible minorities and Indigenous/Aboriginal persons. It is also notable that much of the SME literature, as well as this study, define gender as a dichotomous (women/female, men/male) attribute. Comparing women/female and men/males implicitly assumes within group homogeneity. Future research should use a more inclusive definition of gender. Research is also required to inform about the obstacles to government procurement among the population of SMEs that were unsuccessful in their bids. Practical implications The study provides benchmarks on, and directions to, enhance the participation of women-owned SMEs or enterprises in public procurement. Strategies to support women-owned small businesses that comply with United Nations Sustainable Development Goals are advanced. Social implications The study offers insights to reconcile economic efficiency and social (gender equity) policy goals in the context of public procurement. The “policy-practice divides” in public procurement and women’s enterprise policies are discussed. Originality/value The study is among the first to use a feminist lens to examine the associations between gender of SME ownership and public procurement, while controlling for other salient owner and firm attributes.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".