Bibliographic record
Abstract
What determines firm heterogeneity? What are the consequences of this heterogeneity for the macroeconomy? Traditionally, economists have considered a representative firm as an approximation for reality. Although such a restriction can be useful to study some questions, in reality there is a great deal of heterogeneity in firm behaviour. In this work, I look at different dimensions of heterogeneity in outcomes for firms, their sources and their implications for the macroeconomy. In Chapter 1, I propose a rich general equilibrium model of entrepreneurship, where I allow both wage workers and unemployed to start firms. I show that in this framework, the lower opportunity cost of entrepreneurship for the unemployed induces the formation of lower quality firms relative to wage workers. Using a new confidential owner-employer-employee matched dataset from Canada I test these predictions by verifying that firms created by the unemployed are on average smaller and die faster. I test the mechanism behind this result, by verifying that workers are more responsive to wage changes in their decision to start a firm relative to the unemployed. Finally, I use this framework to evaluate the impact on the economy of a public policy that promotes entrepreneurship among the unemployed. In the model presented in Chapter 2, we study the choice of an individual to start a firm as a function of their outside option as an unemployed and the implications for the efficient allocation in the economy. We show that by simply adding this additional margin to an otherwise standard general equilibrium theoretical framework, wage comparative statics become richer and the efficient allocation chosen by a benevolant social planner has a new interpretation. The chapter highlights the importance of modelling the entry margin into firm ownership in determining firm heterogeneity as well as wage dynamics. In the last chapter, we turn to the study of determinants of a firm's decision of which contract to offer a worker and the implications for wage dynamics and worker retention. We verify empirically that, due to a worker retention motive, match quality affects contract choice and wage cyclicality.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".