What Do Quarterly Workforce Dynamics Tell Us About Wal-Mart? Evidence from New Stores in Pennsylvania
Bibliographic record
Abstract
In this paper I seek to better inform debate regarding Wal-Mart’s local impact on wages, and employment dynamics by combining data on Wal-Mart stores with the recently release Quarterly Workforce Indicators provided by the US Census. Use a panel of Pennsylvania counties, who saw entrance of a Wal-Mart in 2002, I find a new store has no effect on existing employee wages in the retail sector. However, new retail sector hires experience a roughly $0.50 an hour increase in total compensation in the quarter Wal-Mart enters. The entrance of a Wal- Mart draws employees from existing businesses, reducing job creation while increasing net job flows. Wal-Mart also has a longer term effect on net employment of a little more than 50 jobs in a total year. This employment finding is quite similar to findings in Hicks and Wilburn [2001] and Basker [2005]. Perhaps most importantly, Wal-Mart entrance is associated with a dramatic decline in retail sector job turnovers (over 40 percent). This result challenges much of the received wisdom of Wal-Mart’s role in the retail sector. The policy implications of these findings echo those of Ken Stone, who cautions against activist policy in support, or against Wal-Mart at the local level. Disclosure: The author of this study owns no stock in Wal-Mart or any related firm (other than that held by the mutual fund companies Vanguard and TIAA-CREF). I have performed no paid consulting services from any retail firm, its developers, local governments or related entities since 2002 (though I continue to field frequent questions on my earlier research). I have received no honoraria related to Wal-Mart research (other than travel costs paid by the Federal Reserve Bank of Richmond in 2001). In short, except for roughly $1, 500 purchases of diapers annual since 1999 I have no financial relationship with Wal-Mart or any affiliate that I am aware of.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".